Scanning electron microscopic observations on micro-organisms in the root nodules of Tribulus terrestris L. (Zygophyllaceae)

Scanning electron microscopic observations were made on the micro-organisms of root nodules of Tribulus terrestris L. The results showed that nodules of T. terrestris contained dual infection consisting of Rhizobium sp. and Newmania karachiensis. Based on these observations, T. terrestris should be grouped with nonlegume Parasponia-type bacterial nodules.     

Measurement and mathematical modelling of nutrient level and water quality parameters

Physico-chemical water quality parameters and nutrient levels such as water temperature, turbidity, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, conductivity, total nitrogen and total phosphorus, were measured from April to September 2011 in the Karaj dam area, Iran. Total nitrogen in water was modelled using an artificial neural network system. In the proposed system, water temperature, depth, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, turbidity and conductivity were considered as input data, and the total nitrogen in water was considered as output. The weights and biases for various systems were obtained by the quick propagation, batch back propagation, incremental back propagation, genetic and Levenberg-Marquardt algorithms. The proposed system uses 144 experimental data points; 70% of the experimental data are randomly selected for training the network and 30% of the data are used for testing. The best network topology was obtained as (9-5-1) using the quick propagation method with tangent transform function. The average absolute deviation percentages (AAD%) are 2.329 and 2.301 for training and testing processes, respectively. It is emphasized that the results of the artificial neural network (ANN) model are compatible with the experimental data.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

Multiple model bank selection based on nonlinearity measure and H-gap metric

This paper provides a systematic method for model bank selection in multi-linear model analysis for nonlinear systems by presenting a new algorithm which incorporates a nonlinearity measure and a modified gap based metric. This algorithm is developed for off-line use, but can be implemented for on-line usage. Initially, the nonlinearity measure analysis based on the higher order statistic (HOS) and the linear cross correlation methods are used for decomposing the total operating space into several regions with linear models. The resulting linear models are then used to construct the primary model bank. In order to avoid unnecessary linear local models in the primary model bank, a gap based metric is introduced and applied in order to merge similar linear local models. In order to illustrate the usefulness of the proposed algorithm, two simulation examples are presented: a pH neutralization plant and a continuous stirred tank reactor (CSTR).     

High-feed turning of AISI D2 tool steel using multi-radii tool inserts: Tool life,material removed,and workpiece surface integrity evaluation

Multi-radii tool inserts offer novel configuration that comprises of multiple radii at tool nose. A review of the available literature indicates that there exists a need for experimental investigation on certain key machining characteristics of such tools. This paper reports on tool wear/life, material removed, and workpiece surface roughness when multi-radii mixed alumina TiN coated tool inserts are employed for turning D2 steel. Inserts of three different nose radii (0.40, 0.80, 1.20?mm) at six levels of feed rates (ranging from 0.157 to 0.562?mm/rev) are used. Results show that flank wear is the dominant wear mode with catastrophic tool failure occurring at highest nose radius (1.20?mm) and feed rate (0.562?mm/rev) combination. Also, there is ～59% reduction in tool life accompanied by ～62% increase in quantity of material removed as the feed rate increases from 0.157 to 0.562?mm/rev at maximum nose radius (1.20?mm). Feed rate is found to be statistically significant factor for all three responses considered herein at 95% confidence level. Surface integrity assessment at maximum feed rate reveals presence of a strain hardened layer extending to the depth of 150?µm below the machined surface without any observance of white layer for all the tool conditions and nose radius.     

Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

Plasma Internal Inductance in the Presence of External Resonant Fields in IR-T1 Tokamak

In this paper we presented experimental investigation of effects of local limiter biasing (V_biasing = +200 v, V_biasing = +320 v) on the plasma parameters as plasma current, loop voltage, poloidal beta, plasma pressure, plasma energy, plasma resistance, plasma temperature, plasma displacement, Shafranov parameter and plasma internal inductance in IR-T1 tokamak. For these purposes, array of magnetic probes and also a diamagnetic loop have been used. The results show that applied biased voltage V_biasing = +200 v causes to decrease of about 40 % in plasma internal inductance. The plasma resistance and the plasma displacement have been decreased by V_biasing = +200 v. The main result of the application of V_biasing = +200 v is flatting the plasma parameters profiles. In other words, the addition of biasing voltage V_biasing = +200 v to plasma could be effective for improving the quality of tokamak plasma discharge by creating the steady state plasma. The plasma current, plasma pressure, plasma energy, plasma temperature and shift parameter have increased after the application of limiter biasing with V_biasing = +320 v but they decrease rapidly.     

Numerical Simulation of the Mechanical Properties of Sintered and Bonded Perforated Hollow Sphere Structures (PHSS)

This paper investigated the uniaxial mechanical properties of a new type of hollow sphere structures. For this new type, the sphere shell was perforated by several holes in order to open the inner sphere volume and surface. The mechanical properties, i.e. elastic properties and initial yield stress of perforated hollow sphere structures (PHSS) in a primitive cubic arrangement were numerically evaluated for different hole diameters and different joining techniques of the hollow spheres. The results are compa...