Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

Background: techniques of image analysis have been used extensively to minimize interobserver variation of immunohistochemical scoring, yet; image acquisition procedures are often demanding, expensive and laborious. This study aims to assess the validity of image analysis to predict human observer’s score with a simplified image acquisition technique. Materials and methods: formalin fixed- paraffin embedded tissue sections for ameloblastomas and basal cell carcinomas were immunohistochemically stained with monoclonal antibodies to MMP-2 and MMP-9. The extent of antibody positivity was quantified using Imagej® based application on low power photomicrographs obtained with a conventional camera. Results of the software were employed

Soil compaction is one of the most harmful elements affecting soil structure, limiting plant growth and agricultural productivity. It is crucial to assess the degree of soil penetration resistance to discover solutions to the harmful consequences of compaction. In order to obtain the appropriate value, using soil cone penetration requires time and labor-intensive measurements. Currently, satellite technologies, electronic measurement control systems, and computer software help to measure soil penetration resistance quickly and easily within the precision agriculture applications approach. The quantitative relationships between soil properties and the factors affecting their diversity contribute to digital soil mapping. Digital soil maps use

Modern emerged technologies impose development and fabrication of miniatur-ized parts and devices in the micro- and nano-scale. Producing micro- and nano-featured structures requires nonconventional machining processes where con-ventional machining processes such as grinding, milling and eroding have failed. New emerging processes, such laser machining processes, are still fraught with almost invincible processes. Micro-/nano-machining are the pro-cesses of producing parts, microsystems or features at a scale of a few microm-eters and less than one hundred nanometers, respectively. Precise cutting and clean material removal accompanied with a negligible heat affected zone (HAZ), which are usually the characteristics of laser ablation, have

A number of glow discharge experiments has been carried out in a relatively large-volume metallic vacuum chamber containing argon at low pressure and immersed in an inhomogeneous magnetic field generated by a solenoidal coil capable of delivering 2100G. Two Paschen curves demonstrating the dependence of the discharge voltage on sparking parameter Pd and magnetic field strength B were deduced. A graphical correlation showing the behaviour of the voltage difference from the two curves on the ratio B/Pd was constructed. Investigations showed a reduction in the nominal impedance of the discharge device of nearly 20% when B reaches a value of 525G. Plasma confinement regions were found around the internal surface of the chamber at the entranc

        In this study multi objective optimization is utilized to optimize a turning operation to reveal the appropriate level of process features. The goal of this work is to evaluate the optimal combination of cutting parameters like feed, spindle speed, inclination angle and workpiece material to have a best surface quality Taguchi technique L9 mixed orthogonal array, has been adopted to optimize the roughness of surface. Three rods of length around (200 mm) for the three metals are used for this work. Each rod is divided into three parts with 50 mm length. For brass the optimum parametric mix for minimum Ra is A1, B1 and C3, i.e., at tool inclination angle (5), feedrate of 0.01, spindle speed of 120

Abstract

 This study investigated the optimization of wear behavior of AISI 4340 steel based on the Taguchi method under various testing conditions. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the wear rate in 4340 steel. A back-propagation neural network (BPNN) was developed to predict the wear rate. In the development of a predictive model, wear parameters like sliding speed, applying load and sliding distance were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the wear rate. Finally, the Taguchi approach was applied to determine