Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

Electrostatic effects in filamentous protein aggregation.

Electrostatic forces play a key role in mediating interactions between proteins. However, gaining quantitative insights into the complex effects of electrostatics on protein behavior has proved challenging, due to the wide palette of scenarios through which both cations and anions can interact with polypeptide molecules in a specific manner or can result in screening in solution. In this article, we have used a variety of biophysical methods to probe the steady-state kinetics of fibrillar protein self-assembly in a highly quantitative manner to detect how it is modulated by changes in solution ionic strength. Due to the exponential modulation of the reaction rate by electrostatic forces, this reaction represents an exquisitely sensitive probe of these effects in protein-protein interactions. Our approach, which involves a combination of experimental kinetic measurements and theoretical analysis, reveals a hierarchy of electrostatic effects that control protein aggregation. Furthermore, our results provide a highly sensitive method for the estimation of the magnitude of binding of a variety of ions to protein molecules.