Two Analogues of Chloramphenicol

The preparation of D, L- threo-1-[p-(p\u27-nitrophenoxy)-phenyl]-2-dichloroacetamido-1,3-propanediol and D, L-threo-1-[p -(o\u27 - nitrophenoxy)-phenyl]-2-dichloroacetamido-1,3-propanediol is described. Compared with chloramphenicol these compounds showed no interesting activity in antibacterial tests

Two Analogues of Chloramphenicol

The preparation of D, L- threo-1-[p-(p\u27-nitrophenoxy)-phenyl]-2-dichloroacetamido-1,3-propanediol and D, L-threo-1-[p -(o\u27 - nitrophenoxy)-phenyl]-2-dichloroacetamido-1,3-propanediol is described. Compared with chloramphenicol these compounds showed no interesting activity in antibacterial tests

Investigating diagrammatic reasoning with deep neural networks

Diagrams in mechanised reasoning systems are typically en- coded into symbolic representations that can be easily processed with rule-based expert systems. This relies on human experts to define the framework of diagram-to-symbol mapping and the set of rules to reason with the symbols. We present a new method of using Deep artificial Neu- ral Networks (DNN) to learn continuous, vector-form representations of diagrams without any human input, and entirely from datasets of dia- grammatic reasoning problems. Based on this DNN, we developed a novel reasoning system, Euler-Net, to solve syllogisms with Euler diagrams. Euler-Net takes two Euler diagrams representing the premises in a syl- logism as input, and outputs either a categorical (subset, intersection or disjoint) or diagrammatic conclusion (generating an Euler diagram rep- resenting the conclusion) to the syllogism. Euler-Net can achieve 99.5% accuracy for generating syllogism conclusion. We analyse the learned representations of the diagrams, and show that meaningful information can be extracted from such neural representations. We propose that our framework can be applied to other types of diagrams, especially the ones we don’t know how to formalise symbolically. Furthermore, we propose to investigate the relation between our artificial DNN and human neural circuitry when performing diagrammatic reasoning

Flight Results of the Attitude Determination and Control System for the NEMO-HD Earth Observation Microsatellite

NEMO-HD is an Earth observation microsatellite designed and built at the Space Flight Laboratory at the University of Toronto Institute for Aerospace Studies (SFL) in collaboration with the Slovenian Centre of Excellence for Space Sciences and Technologies (SPACE-SI) who owns and operates the spacecraft. The mission was launched successfully into a circular Sun-synchronous orbit with 10:30 LTDN at an altitude of 535 km, aboard the VEGA VV16 mission from French Guiana on September 2, 2020. The primary payload is an optical imager, providing still imagery on its panchromatic (PAN) channel with 2.8 m ground sample distance (GSD), 5.6 m GSD on its four multi-spectral channels (R,G,B,NIR), and high definition video with 1920x1080 resolution. To achieve the precise pointing and stability requirements required for high quality optical imagery, the spacecraft is three-axis stabilized using reaction wheels for attitude control, and dual star trackers for attitude determination. The spacecraft has three targeting modes for imaging: inertial pointing, nadir-pointing, and ground target tracking; the exact mode selection depends upon the type of imagery desired. In this paper we discuss spacecraft attitude determination and control system design, and present the detailed attitude determination and control system pointing performance results for the mission in each of the primary operational modes, using one of the two star trackers as the “true” reference attitude

Photonic band structure of highly deformable, self-assembling systems

We calculate the photonic band structure at normal incidence of highly deformable, self-assembling systems - cholesteric elastomers subjected to external stress. Cholesterics display brilliant reflection and lasing owing to gaps in their photonic band structure. The band structure of cholesteric elastomers varies sensitively with strain, showing new gaps opening up and shifting in frequency. A novel prediction of a total band gap is made, and is expected to occur in the vicinity of the previously observed de Vries bandgap, which is only for one polarisation

Simulation and analysis of grating-integrated quantum dot infrared detectors for spectral response control and performance enhancement

We propose and analyze a novel detector structure for pixel-level multispectral infrared imaging. More specifically, we investigate the device performance of a grating-integrated quantum dots-in-a-well photodetector under backside illumination. Our design uses 1-dimensional grating patterns fabricated directly on a semiconductor contact layer and, thus, adds a minimal amount of additional effort to conventional detector fabrication flows. We show that we can gain wide-range control of spectral response as well as large overall detection enhancement by adjusting grating parameters. For small grating periods, the spectral responsivity gradually changes with parameters. We explain this spectral tuning using the Fabry-Perot resonance and effective medium theory. For larger grating periods, the responsivity spectra get complicated due to increased diffraction into the active region, but we find that we can obtain large enhancement of the overall detector performance. In our design, the spectral tuning range can be larger than 1 mu m, and, compared to the unpatterned detector, the detection enhancement can be greater than 92% and 148% for parallel and perpendicular polarizations. Our work can pave the way for practical, easy-to-fabricate detectors, which are highly useful for many infrared imaging applications. (C) 2014 AIP Publishing LLCopen1

Fundamental limits of super-resolution microscopy by dielectric microspheres and microfibers

In recent years, optical super-resolution by microspheres and microfibers emerged as a new paradigm in nanoscale label-free and fluorescence imaging. However, the mechanisms of such imaging are still not completely understood and the resolution values are debated. In this work, the fundamental limits of super-resolution imaging by high-index barium-titanate microspheres and silica microfibers are studied using nanoplasmonic arrays made from Au and Al. A rigorous resolution analysis is developed based on the object's convolution with the point-spread function that has width well below the conventional (∼λ/2) diffraction limit, where λ is the illumination wavelength. A resolution of ∼λ/6-λ/7 is demonstrated for imaging nanoplasmonic arrays by microspheres. Similar resolution was demonstrated for microfibers in the direction perpendicular to the fiber axis with hundreds of times larger field-of-view in comparison to microspheres. Using numerical solution of Maxwell's equations, it is shown that extraordinary close point objects can be resolved in the far field, if they oscillate out of phase. Possible super-resolution using resonant excitation of whispering gallery modes is also studied. Keywords: Optical super-resolution; near-field microscopy; confocal microscop