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...     

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.     

Ecological Planting Imperative: Functional Systems,Not Stylized Ecologies

The ability of landscape architectural projects to mitigate the worst effects of climate change will depend upon designed ecological systems. These systems will be built with plants. Despite the recognition of ecology as an essential driver of landscapes, the professionals of landscape architecture too often lack the knowledge and practical skills to create robust vegetative systems. New approaches and tools are required. This article outlines principles and methods for designing biodiverse plant systems for urban sites. Planting methods that increase species richness, functional diversity, and spatial complexity are emphasized as a way of developing more resilient plantings. Selecting species with similar evolutionary adaptions to stress, disturbance, and competition—as well as creating multi-layered compositions of diverse plant morphologies—allows designers to create compatible, long-lived plant mixes. To balance the increased visual complexity of diverse plant mixes, the article explores design techniques to make plantings more appealing to the public. The strategies explored here are based on the projects, experience, and research of Phyto Studio, a Washington, D.C. based studio. The methods build on work described in the author’s book, Planting in a Post-Wild World, an exploration of how to create designed plant communities.     

Discovery of GPR183 Agonists Based on an Antagonist Scaffold

The G protein-coupled receptor GPR183/EBI2, which is activated by oxysterols, is a therapeutic target for inflammatory and metabolic diseases where both antagonists and agonists are of potential interest. Using the piperazine diamide core of the known GPR183 antagonist (E)-3-(4-bromophenyl)-1-(4-(4-methoxybenzoyl)piperazin-1-yl)prop-2-en-1-one (NIBR189) as starting point, we identified and sourced 79 structurally related compounds that were commercially available. In vitro screening of this compound collection using a Ca²⁺ mobilization assay resulted in the identification of 10 compounds with agonist properties. To enable establishment of initial structure-activity relationship trends, these were supplemented with five in-house compounds, two of which were also shown to be GPR183 agonists. Taken together, our findings suggest that the agonist activity of this compound series is dictated by the substitution pattern of one of the two distal phenyl rings, which functions as a molecular efficacy-switch.     

Ratiometric Detection of Glutathione Based on Disulfide Linkage Rupture between a FRET Coumarin Donor and a Rhodamine Acceptor

Abnormal levels of glutathione, a cellular antioxidant, can lead to a variety of diseases. We have constructed a near-infrared ratiometric fluorescent probe to detect glutathione concentrations in biological samples. The probe consists of a coumarin donor, which is connected through a disulfide-tethered linker to a rhodamine acceptor. Under the excitation of the coumarin donor at 405 nm, the probe shows weak visible fluorescence of the coumarin donor at 470 nm and strong near-infrared fluorescence of the rhodamine acceptor at 652 nm due to efficient Forster resonance energy transfer (FRET) from the donor to the acceptor. Glutathione breaks the disulfide bond through reduction, which results in a dramatic increase in coumarin fluorescence and a corresponding decrease in rhodamine fluorescence. The probe possesses excellent cell permeability, biocompatibility, and good ratiometric fluorescence responses to glutathione and cysteine with a self-calibration capability. The probe was utilized to ratiometrically visualize glutathione concentration alterations in HeLa cells and Drosophila melanogaster larvae.     

In Vivo Albumin-Binding of a C-Functionalized Cyclam Platform for 64Cu-PET/CT Imaging in Breast Cancer Model

An improved glucose-chelator-albumin bioconjugate (GluCAB) derivative, GluCAB-2^Mal, has been synthesized and studied for in vivo ⁶⁴Cu-PET/CT imaging in breast cancer mice models together with its first-generation analogue GluCAB-1^Mal. The radioligand works on the principle of tumor targeting through the enhanced permeability and retention (EPR) effect with a supportive role played by glucose metabolism. [⁶⁴Cu]Cu-GluCAB-2^Mal (99 % RCP) exhibited high serum stability with immediate binding to serum proteins. In vivo experiments for comparison between tumor targeting of [⁶⁴Cu]Cu-GluCAB-2^Mal and previous-generation [⁶⁴Cu]Cu-GluCAB-1^Mal encompassed microPET/CT imaging and biodistribution analysis in an allograft E0771 breast cancer mouse model. Tumor uptake of [⁶⁴Cu]Cu-GluCAB-2^Mal was clearly evident with twice as much accumulation as compared to its predecessor and a tumor/muscle ratio of up to 5 after 24 h. Further comparison indicated a decrease in liver accumulation for [⁶⁴Cu]Cu-Glu-CAB-2^Mal.