Rensselaer Polytechnic Institute

About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Finite element method. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

Silver-nanoparticle-embedded antimicrobial paints based on vegetable oil

Abstract: Developing bactericidal coatings using simple green chemical methods could be a promising route to potential environmentally friendly applications. Here, we describe an environmentally friendly chemistry approach to synthesize metal-nanoparticle (MNP)-embedded paint, in a single step, from common household paint. The naturally occurring oxidative drying process in oils, involving free-radical exchange, was used as the fundamental mechanism for reducing metal salts and dispersing MNPs in the oil media, without the use of any external reducing or stabilizing agents. These well-dispersed MNP-in-oil dispersions can be used directly, akin to commercially available paints, on nearly all kinds of surface such as wood, glass, steel and different polymers. The surfaces coated with silver-nanoparticle paint showed excellent antimicrobial properties by killing both Gram-positive human pathogens (Staphylococcus aureus) and Gram-negative bacteria (Escherichia coli). The process we have developed here is quite general and can be applied in the synthesis of a variety of MNP-in-oil systems.

Bank performance, efficiency and ownership in transition countries

Abstract: Using data from 1996 to 2000, we investigate the effects of ownership, especially by a strategic foreign owner, on bank efficiency for eleven transition countries in an unbalanced panel consisting of 225 banks and 856 observations. Applying stochastic frontier estimation procedures, we compute profit and cost efficiency taking account of both time and country effects directly. In second-stage regressions, we use the efficiency measures along with return on assets to investigate the influence of ownership type. With respect to the impact of ownership, we conclude that privatization by itself is not sufficient to increase bank efficiency as government-owned banks are not appreciably less efficient than domestic private banks. We find that foreign-owned banks are more cost-efficient than other banks and that they also provide better service, in particular if they have a strategic foreign owner. The remaining government-owned banks are less efficient in providing services, which is consistent with the hypothesis that the better banks were privatized first in transition countries.

Miniaturized gas ionization sensors using carbon nanotubes

Abstract: Gas sensors operate by a variety of fundamentally different mechanisms1,2,3,4,5,6,7,8,9,10,11,12,13,14. Ionization sensors13,14 work by fingerprinting the ionization characteristics of distinct gases, but they are limited by their huge, bulky architecture, high power consumption and risky high-voltage operation. Here we report the fabrication and successful testing of ionization microsensors featuring the electrical breakdown of a range of gases and gas mixtures at carbon nanotube tips. The sharp tips of nanotubes generate very high electric fields at relatively low voltages, lowering breakdown voltages several-fold in comparison to traditional electrodes, and thereby enabling compact, battery-powered and safe operation of such sensors. The sensors show good sensitivity and selectivity, and are unaffected by extraneous factors such as temperature, humidity, and gas flow. As such, the devices offer several practical advantages over previously reported nanotube sensor systems15,16,17. The simple, low-cost, sensors described here could be deployed for a variety of applications, such as environmental monitoring, sensing in chemical processing plants, and gas detection for counter-terrorism.

The Field of Streams: Sagittarius and its Siblings

Abstract: We use Sloan Digital Sky Survey (SDSS) Data Release 5 (DR5) u, g, r, i, z photometry to study Milky Way halo substructure in the area around the north Galactic cap. A simple color cut (g - r < 0.4) reveals the tidal stream of the Sagittarius dwarf spheroidal galaxy, as well as a number of other stellar structures in the field. Two branches (A and B) of the Sagittarius stream are clearly visible in an RGB composite image created from three magnitude slices, and there is also evidence for a still more distant wrap behind the A branch. A comparison of these data with numerical models suggests that the shape of the Galactic dark halo is close to spherical.

Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

Abstract: The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists’ judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.