Rationalizing spacing requirements for nailed wood connections by induced wood splitting

1992 ◽  
Vol 19 (5) ◽  
pp. 842-846
Author(s):  
Peter W. C. Lau

Nails of five different types and sizes were driven through test specimens cut from 35 × 85 mm Douglas-fir and eastern spruce (spruce-pine-fir) dimension lumber, preconditioned to 12% moisture content. Each specimen received either a single nail or two identical nails, spaced at 10 times the nail diameter for common and spiral nails, or at 25 mm for glulam rivets. Nail-induced crack size was evaluated using digital imaging techniques. These crack data were analysed together with the data on single-nail specimens obtained in a parallel study. This permitted the development of a model predicting mean crack length on the basis of spacing between nails. This model was used to generate the spacing requirements based on a criterion on critical stress for fracture, and on crack length. Key words: nailing, wood connections, cracking, nail spacing.

1998 ◽  
Vol 4 (S2) ◽  
pp. 56-57
Author(s):  
Bruce D. Newell

Color and Color PerceptionAccurate color reproduction is an essential component in the effective use of digital imaging techniques in light microscopy. The color reproduction process begins with an understanding that color is the result of three key elements; light, the illuminated object, and the observation method. When light strikes an object, wavelengths may be reflected, absorbed or transmitted. Additionally, the observed colors are strongly influenced by the intensity of the illumination and its spectral content. Colors we think of as “white” can vary significantly in their spectral distribution, e.g., skylight is actually a bluish white while tungsten bulbs produce a yellowish white.Light waves that reach the eye stimulate a complex process that is not yet fully understood. Within the retina, three different types of cones respond to color hues and brightness while rods sense only brightness.


Author(s):  
Xiao Zhang

Polymer microscopy involves multiple imaging techniques. Speed, simplicity, and productivity are key factors in running an industrial polymer microscopy lab. In polymer science, the morphology of a multi-phase blend is often the link between process and properties. The extent to which the researcher can quantify the morphology determines the strength of the link. To aid the polymer microscopist in these tasks, digital imaging systems are becoming more prevalent. Advances in computers, digital imaging hardware and software, and network technologies have made it possible to implement digital imaging systems in industrial microscopy labs.


Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3874
Author(s):  
Dominika Veselinyová ◽  
Jana Mašlanková ◽  
Katarina Kalinová ◽  
Helena Mičková ◽  
Mária Mareková ◽  
...  

We are experiencing rapid progress in all types of imaging techniques used in the detection of various numbers and types of mutation. In situ hybridization (ISH) is the primary technique for the discovery of mutation agents, which are presented in a variety of cells. The ability of DNA to complementary bind is one of the main principles in every method used in ISH. From the first use of in situ techniques, scientists paid attention to the improvement of the probe design and detection, to enhance the fluorescent signal intensity and inhibition of cross-hybrid presence. This article discusses the individual types and modifications, and is focused on explaining the principles and limitations of ISH division on different types of probes. The article describes a design of probes for individual types of in situ hybridization (ISH), as well as the gradual combination of several laboratory procedures to achieve the highest possible sensitivity and to prevent undesirable events accompanying hybridization. The article also informs about applications of the methodology, in practice and in research, to detect cell to cell communication and principles of gene silencing, process of oncogenesis, and many other unknown processes taking place in organisms at the DNA/RNA level.


2017 ◽  
Vol 2 (2) ◽  
pp. 37 ◽  
Author(s):  
Antonio Marcelino Silva Filho ◽  
Carlos Leandro Borges Silva ◽  
Marco Antonio Assfalk Oliveira ◽  
Thyago Gumeratto Pires ◽  
Aylton José Alves ◽  
...  

This paper presents the study of the relationship between electrical properties and physical characteristics of the soil. Measures of apparent electrical resistivity of the soil were made for different types of soil, varying moisture content gradually while maintaining a constant compaction, and then varying the compaction and relating it to a constant humidity. Development of a correlation surface is proposed in order to identify granulometry of the soil from moisture and compaction measurements. For the study of spatial variability, two areas were chosen to allow the change of moisture content and compaction in order to verify the measurement capacity of apparent electrical resistivity of the soil as methodology to identify change in soil dynamics. Results obtained show correlations among apparent electrical resistivity of the soil, moisture, soil compaction and clay content.


Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.


1999 ◽  
Vol 60 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Cs. Szabo ◽  
L. Babinszky ◽  
M.W.A. Verstegen ◽  
O. Vangen ◽  
A.J.M. Jansman ◽  
...  

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