Synthesis of silicon-substituted hydroxyapatite by a hydrothermal method with two different phosphorous sources

Silicon-substituted hydroxyapatite (Si-HA) with up to 1.8 wt% Si content was prepared successfully by a hydrothermal method, using Ca(NO₃)₂, (NH₄)₃PO₄ or (NH₄)₂HPO₄ and Si(OCH₂CH₃)₄ (TEOS) as starting materials. Silicon has been incorporated in hydroxyapatite (HA) lattice by partially replacing phosphate (PO₄³⁻) groups with silicate (SiO₄⁴⁻) groups resulting in Si-HA described as Ca₁₀(PO₄)_6−x(SiO₄)_x(OH)_2−x. X-ray diffraction (XRD), Fourier transform IR spectroscopy (FTIR), inductively coupled plasma AES (ICP-AES) and scanning electron microscopy (SEM) techniques reveal that the substitution of phosphate groups by silicate groups causes some OH⁻ loss to maintain the charge balance and changes the lattice parameters of HA. The crystal shape of Si-HA has not altered compared to silicon-free reference hydroxyapatite but Si-incorporation reduces the size of Si-HA crystallites. Based on in vitro tests, soaking the specimens in simulated body fluid (SBF), and MTT assays by human osteoblast-like cells, Si-substituted hydroxyapatite is more bioactive than pure hydroxyapatite.     

Forecasting Economic Data with Neural Networks

Studies in recent years have attempted to forecast macroeconomic phenomena with neural networks reporting mixed results. This work represents an investigation of this problem using U.S. Real Gross Domestic Production and Industrial Production as case studies. This work is based on a coefficient of determination which accurately measures the ability of linear or nonlinear models to forecast economic data. The significance of our work is twofold: (1) It confirms recent work that neural networks significantly outperform linear regression due to nonlinearities inherent in the data sets, and (2) it provides a systematic approach that guarantees to find the maximum correlation between input(s) and output of interest. JEL Classification: C45, C52, C53, C67,     

Analog fault diagnosis of actual circuits using neural networks

We have developed a neural-network based analog fault diagnostic system for actual circuits. Our system uses a data acquisition board to excite a circuit with an impulse and sample its output to collect training data for the neural network. The collected data is preprocessed by wavelet decomposition, normalization, and principal component analysis (PCA) to generate optimal features for training the neural network. This ensures a simple architecture for the neural network and minimizes the size of the training set required for its proper training. Our studies indicate that features extracted from actual circuits lie closer to each other and exhibit more overlap across fault classes compared to SPICE simulations. This implies that the neural network architecture which can most reliably perform fault diagnosis of actual circuits is one whose outputs estimate the probabilities that input features belong to different fault classes. Our work also shows that SPICE simulations can be used to select appropriate features for training the neural network. Reliable diagnosis of faults in an actual circuit, however, requires training data from the circuit itself. Our fault diagnostic system, trained and tested using data obtained from real sample circuits, achieves 95% accuracy in classifying faulty components     

Exploring the potential of milkweed stalk in wood plastic manufacture

The physical and mechanical properties of milkweed composites based on different loads of milkweed flour and maleic anhydride grafted polypropylene (MAPP) using polypropylene as matrix are investigated in this study. There levels of milkweed fibers (30, 40, and 50 wt.%), one level of mixed milkweed flour (20:20 wt.% fiber:bark), and two levels of MAPP (4 and 6 wt.%) were used to prepare natural fiber-reinforced composites. Physical and mechanical properties including flexural, tension, impact, and thickness swelling were evaluated according to ASTM standards. The result demonstrated that addition of milkweed flour fluctuates mechanical properties of reinforced composite. However, the optimum load of milkweed flour was different in each test. Generally, 40 wt.% mixed flour composite in comparison with 40 wt.% milkweed composite showed lower mechanical results and higher thickness swelling. MAPP as a coupling agent improved physical and mechanical properties of milkweed-filled composites in most properties. The results of this study depicted positive effects of lignocellulose fibers and coupling gent and also negative effect of bark flour as a function of lower cellulose and higher extractive contents on physical and mechanical properties of milkweed-reinforced composites.     

Capturing human motion using body‐fixed sensors: outdoor measurement and clinical applications

Motion capture is mainly based on standard systems using optic, magnetic or sonic technologies. In this paper, the possibility to detect useful human motion based on new techniques using different types of body‐fixed sensors is shown. In particular, a combination of accelerometers and angular rate sensors (gyroscopes) showed a promising design for a hybrid kinematic sensor measuring the 2D kinematics of a body segment. These sensors together with a portable datalogger, and using simple biomechanical models, allow capture of outdoor and long‐term movements and overcome some limitations of the standard motion capture systems. Significant parameters of body motion, such as nature of motion (postural transitions, trunk rotation, sitting, standing, lying, walking, jumping) and its spatio‐temporal features (velocity, displacement, angular rotation, cadence and duration) have been evaluated and compared to the camera‐based system. Based on these parameters, the paper outlines the possibility to monitor physical activity and to perform gait analysis in the daily environment, and reviews several clinical investigations related to fall risk in the elderly, quality of life, orthopaedic outcome and sport performance. Taking advantage of all the potential of these body‐fixed sensors should be promising for motion capture and particularly in environments not suitable for standard technology such as in any field activity. Copyright © 2004 John Wiley & Sons, Ltd.     

Effects of smoke produced from smoldering plants on the Aspergillus flavus growth and production of aflatoxin in pistachio

Aflatoxin (AF) contamination of the Iranian exporting pistachio has become a major problem in the last decades. In this study, the antifungal effects of smoke produced from the smoldering of several herbal plants were investigated. Four different ratios of plant weight/exposure time (5/5, 10/15, 15/30, and 20 g/45 min) were used from each plant material to smoke two isolates of Aspergillus flavus (A47 and A3), grown in potato dextrose agar (PDA). The results showed that the 20 g/45 min treatment using smoldering cinnamon bark, neem leaves, and clove flowers had efficient inhibitions of 100, 85, and 75%, respectively. Furthermore, the smoking of pistachio inoculated with a spore concentration of 1 × 10⁶ / ml using 30 g of cinnamon bark, neem leaves, and clove flowers for 75 min was capable of preventing the production of different types of AF, that is, B1, B2, G1, and G2 in the treated products. There is a good potential to smoke pistachio with these three herbal plants and prevent the production of AF in pistachio during the handling, storage, and transportation.     

CapsNet-based brain tumor segmentation in multimodal MRI images using inhomogeneous voxels in Del vector domain

Multimedia Tools and Applications - Glioma is a type of brain tumor that is the most typical and most aggressive tumor. Magnetic resonance imaging (MRI) has a widespread utilization as an imaging...     

New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach

This paper presents an innovative machine learning approach for the formulation of load carrying capacity of castellated steel beams (CSB). New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA. The load capacity was formulated in terms of the geometrical and mechanical properties of the castellated beams. An extensive trial study was carried out to select the most relevant input variables for the LGP and GSA models. A comprehensive database was gathered from the literature to develop the models. The generalization capabilities of the models were verified via several criteria. The sensitivity of the failure load of CSB to the influencing variables was examined and discussed. The employed machine learning systems were found to be effective methods for evaluating the failure load of CSB. The prediction performance of the optimal LGP model was found to be better than that of the GSA model.     

Predicting the vapor-liquid equilibrium of carbon dioxide+alkanol systems by using an artificial neural network

A multi-layer feed-forward artificial neural network has been presented for accurate prediction of the vapor liquid equilibrium (VLE) of CO₂+alkanol mixtures. Different types of alkanols namely, 1-propaol, 2-propanol, 1-butanol, 1-pentanol, 2-pentanol, 1-hexanol and 1-heptanol, are used in this study. The proposed network is trained using the Levenberg-Marquardt back propagation algorithm, and the tan-sigmoid activation function is applied to calculate the output values of the neurons of the hidden layers. According to the network’s training, validation and testing results, a six layer neural network is selected as the best architecture. The presented model is very accurate over wide ranges of experimental pressure and temperatures. Comparison of the suggested neural network model with the most important thermodynamic correlations shows that the proposed neuromorphic model outperforms the other available alternatives. The predicted equilibrium pressure and vapor phase CO₂ mole fraction are in good agreement with experimental data suggesting the accuracy of the proposed neural network model for process design.     

A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

Acidic combustion gases can cause rapid corrosion when they condense on pollution control or energy recovery equipments. Since the potential of sulfuric acid condensation from flue gases is of considerable economic significance, a multi-layer feed forward artificial neural network has been presented for accurate prediction of the flue gas sulfuric acid dew points to mitigate the corrosion problems in process and power plants. According to the network’s training, validation and testing results, a three layer neural network with four neurons in the hidden layer is selected as the best architecture for accurate prediction of sulfuric acid dew points. The presented model is very accurate and reliable for predicting the acid dew points over wide ranges of sulfur trioxide and water vapor concentrations. Comparison of the suggested neural network model with the most important existing correlations shows that the proposed neuromorphic model outperforms the other alternatives both in accuracy and generality. The predicted flue gas sulfuric acid dew points are in excellent agreement with experimental data suggesting the accuracy of the proposed neural network model for predicting the sulfuric acid condensation in stacks, pollution control devices, economizers and flue gas recovery systems in process industries.