Effect of Heat Treatment on the Phase Formation of La0.6Sr0.4CoO3-α

Powder of La0.6Sr0.4CoO3-α (LSCO) was synthesized by a combined citrate-EDTA method. The as-synthesized LSCO powder was calcined, respectively at temperatures of 800, 900 and 1000 °C with different heating/cooling rates which are 2, 5, 10 and 15 °C min-1. The effects of heat treatments on the phase formation of perovskite phase of LSCO were investigated by powder X-ray diffraction (XRD). The XRD patterns revealed that the rate of 5 °C min-1 is the optimum heating/cooling rate to obtain a single perovskite phase of LSCO with calcination temperature of 800 °C. This result was confirmed by a thermogravimetric analysis (TGA) as it showed a complete decomposition of intermediate compounds to form oxide material was also observed at 800 °C.

Design of a Grid for Preparation of high Density Granules from Dispersed Materials

New design of a grid for preparation of high density granules with enhanced mechanical strength by granulation of dispersed materials is suggested. A method for hydrodynamic dimensioning of the grid depending on granulation conditions, hydrodynamic regime of the operation, dispersity and physicochemical characteristics of the materials to be granulated was suggested. The aim of the grid design is to solve the problems arising by the granulation of disperse materials.

Kinematic Modelling and Maneuvering of A 5-Axes Articulated Robot Arm

This paper features the kinematic modelling of a 5-axis stationary articulated robot arm which is used for doing successful robotic manipulation task in its workspace. To start with, a 5-axes articulated robot was designed entirely from scratch and from indigenous components and a brief kinematic modelling was performed and using this kinematic model, the pick and place task was performed successfully in the work space of the robot. A user friendly GUI was developed in C++ language which was used to perform the successful robotic manipulation task using the developed mathematical kinematic model. This developed kinematic model also incorporates the obstacle avoiding algorithms also during the pick and place operation.

A Robust Wheel Slip Controller for a Hybrid Braking System

A robust wheel slip controller for electric vehicles is introduced. The proposed wheel slip controller exploits the dynamics of electric traction drives and conventional hydraulic brakes for achieving maximum energy efficiency and driving safety. Due to the control of single wheel traction motors in combination with a hydraulic braking system, it can be shown, that energy recuperation and vehicle stability control can be realized simultaneously. The derivation of a sliding mode wheel slip controller accessing two drivetrain actuators is outlined and a comparison to a conventionally braked vehicle is shown by means of simulation.

Prediction of the Epileptic Events 'Epileptic Seizures' by Neural Networks and Expert Systems

Many studies have focused on the nonlinear analysis of electroencephalography (EEG) mainly for the characterization of epileptic brain states. It is assumed that at least two states of the epileptic brain are possible: the interictal state characterized by a normal apparently random, steady-state EEG ongoing activity; and the ictal state that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called in neurology, a seizure. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don-t know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. Our approach is to use the neural network tool to detect interictal states and to predict from those states the upcoming seizure ( ictal state). Analysis of the EEG signal based on neural networks is used for the classification of EEG as either seizure or non-seizure. By applying prediction methods it will be possible to predict the upcoming seizure from non-seizure EEG. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. Preictal, ictal, and post ictal EEG recordings are available on such patients for analysis The system will be induced by taking a body of samples then validate it using another. Distinct from the two first ones a third body of samples is taken to test the network for the achievement of optimum prediction. Several methods will be tried 'Backpropagation ANN' and 'RBF'.

UAV Position Estimation Using Remote Radio Head With Adaptive Power Control

The adaptive power control of Code Division Multiple Access (CDMA) communications using Remote Radio Head (RRH) between multiple Unmanned Aerial Vehicles (UAVs) with a link-budget based Signal-to-Interference Ratio (SIR) estimate is applied to four inner loop power control algorithms. It is concluded that Base Station (BS) can calculate not only UAV distance using linearity between speed and Consecutive Transmit-Power-Control Ratio (CTR) of Adaptive Step-size Closed Loop Power Control (ASCLPC), Consecutive TPC Ratio Step-size Closed Loop Power Control (CS-CLPC), Fixed Step-size Power Control (FSPC), but also UAV position with Received Signal Strength Indicator (RSSI) ratio of RRHs.

Removal of Methylene Blue from Aqueous Solution by Using Gypsum as a Low Cost Adsorbent

Removal of Methylene Blue (MB) from aqueous solution by adsorbing it on Gypsum was investigated by batch method. The studies were conducted at 25°C and included the effects of pH and initial concentration of Methylene Blue. The adsorption data was analyzed by using the Langmuir, Freundlich and Tempkin isotherm models. The maximum monolayer adsorption capacity was found to be 36 mg of the dye per gram of gypsum. The data were also analyzed in terms of their kinetic behavior and was found to obey the pseudo second order equation.

Numerical Study of Fluid Mixing in a Grooved Micro-Channel with Wavy Sidewalls

In this work, we perform numerical simulation of fluid mixing in a floor-grooved micro-channel with wavy sidewalls which may impose perturbation on the helical flow induced by the slanted grooves on the channel floor. The perturbation is caused by separation vortices in the recesses of the wavy-walled channel as the Reynolds number is large enough. The results show that the effects of the wavy sidewalls of the present micromixer on the enhancement of fluid mixing increase with the increase of Reynolds number. The degree of mixing increases with the increase of the corrugation angle, until the angle is greater than 45 degrees. Besides, the pumping pressure of the micromixer increases with the increase of the corrugation angle monotonically. Therefore, we would suggest setting the corrugation angle of the wavy sidewalls to be 45 degrees.

Vibration Control of a Cantilever Beam Using a Tunable Vibration Absorber Embedded with ER Fluids

This paper investigates experimental studies on vibration suppression for a cantilever beam using an Electro-Rheological (ER) sandwich shock absorber. ER fluid (ERF) is a class of smart materials that can undergo significant reversible changes immediately in its rheological and mechanical properties under the influence of an applied electric field. Firstly, an ER sandwich beam is fabricated by inserting a starch-based ERF into a hollow composite beam. At the same time, experimental investigations are focused on the frequency response of the ERF sandwich beam. Second, the ERF sandwich beam is attached to a cantilever beam to become as a shock absorber. Finally, a fuzzy semi-active vibration control is designed to suppress the vibration of the cantilever beam via the ERF sandwich shock absorber. To check the consistency of the proposed fuzzy controller, the real-time implementation validated the performance of the controller.

Mixed-Mode Study of Rock Fracture Mechanics by using the Modified Arcan Specimen Test

This paper studies mixed-mode fracture mechanics in rock based on experimental and numerical analyses. Experiments were performed on sharp-cracked specimens using the modified Arcan specimen test loading device. The modified Arcan specimen test was, in association with a special loading device, an appropriate apparatus for experimental mixed-mode fracture analysis. By varying the loading angle from 0° to 90°, pure mode-I, pure mode-II and a wide range of mixed-mode data were obtained experimentally. Using the finite element results, correction factors applied to the rectangular fracture specimen. By employing experimentally measured critical loads and the aid of the finite element method, mixed-mode fracture toughness for the limestone under consideration determined.

An Environmental Impact Tool to Assess National Energy Scenarios

The Long-range Energy and Alternatives Planning (LEAP) energy planning system has been developed for South Africa, for the 2005 base year and a limited number of plausible future scenarios that may have significant implications (negative or positive) in terms of environmental impacts. The system quantifies the national energy demand for the domestic, commercial, transport, industry and agriculture sectors, the supply of electricity and liquid fuels, and the resulting emissions. The South African National Energy Research Institute (SANERI) identified the need to develop an environmental assessment tool, based on the LEAP energy planning system, to provide decision-makers and stakeholders with the necessary understanding of the environmental impacts associated with different energy scenarios. A comprehensive analysis of indicators that are used internationally and in South Africa was done and the available data was accessed to select a reasonable number of indicators that could be utilized in energy planning. A consultative process was followed to determine the needs of different stakeholders on the required indicators and also the most suitable form of reporting. This paper demonstrates the application of Energy Environmental Sustainability Indicators (EESIs) as part of the developed tool, which assists with the identification of the environmental consequences of energy generation and use scenarios and thereby promotes sustainability, since environmental considerations can then be integrated into the preparation and adoption of policies, plans, programs and projects. Recommendations are made to refine the tool further for South Africa.

Topological Queries on Graph-structured XML Data: Models and Implementations

In many applications, data is in graph structure, which can be naturally represented as graph-structured XML. Existing queries defined on tree-structured and graph-structured XML data mainly focus on subgraph matching, which can not cover all the requirements of querying on graph. In this paper, a new kind of queries, topological query on graph-structured XML is presented. This kind of queries consider not only the structure of subgraph but also the topological relationship between subgraphs. With existing subgraph query processing algorithms, efficient algorithms for topological query processing are designed. Experimental results show the efficiency of implementation algorithms.

Design and Simulation of a New Self-Learning Expert System for Mobile Robot

In this paper, we present a novel technique called Self-Learning Expert System (SLES). Unlike Expert System, where there is a need for an expert to impart experiences and knowledge to create the knowledge base, this technique tries to acquire the experience and knowledge automatically. To display this technique at work, a simulation of a mobile robot navigating through an environment with obstacles is employed using visual basic. The mobile robot will move through this area without colliding with any obstacle and save the path that it took. If the mobile robot has to go through a similar environment again, then it will apply this experience to help it move through quicker without having to check for collision.

Hazard Contributing Factors Classification for Petrol Fuel Station

Petrol Fuel Station (PFS) has potential hazards to the people, asset, environment and reputation of an operating company. Fire hazards, static electricity air pollution evoked by aliphatic and aromatic organic compounds are major causes of accident/incident occurrence at fuel station. Activities such as carelessness, maintenance, housekeeping, slips trips and falls, transportation hazard, major and minor injuries, robbery and snake bites has a potential to create unsafe conditions. The level of risk of these hazards varies according to location and country. The emphasis on safety considerations by the government is variable all around the world. Developed countries safety records are much better as compared to developing countries safety statistics. There is no significant approach available to highlight the unsafe acts and unsafe conditions during operation and maintenance of fuel station. Fuel station is the most commonly available facilities that contain flammable and hazardous materials. Due to continuous operation of fuel station they pose various hazards to people, environment and assets of an organization. To control these hazards, there is a need for specific approach. PFS operation is unique as compared to other businesses. For smooth operations it demands an involvement of operating company, contractor and operator group. This study will focus to address hazard contributing factors that have a potential to make PFS operation risky. One year data collected, 902 activities analyzed, comparisons were made to highlight significant contributing factors. The study will provide help and assistance to PFS outlet marketing companies to make their fuel station operation safer. It will help health safety and environment (HSE) professionals to arrest the gap available related to safety matters at PFS.

Examining Herzberg-s Two Factor Theory in a Large Chinese Chemical Fiber Company

The validity of Herzberg-s Two-Factor Theory of Motivation was tested empirically by surveying 2372 chemical fiber employees in 2012. In the valid sample of 1875 respondents, the degree of overall job satisfaction was more than moderate. The most highly valued components of job satisfaction were: “corporate image," “collaborative working atmosphere," and “supervisor-s expertise"; whereas the lowest mean score was 34.65 for “job rotation and promotion." The top three job retention options rated by the participants were “good image of the enterprise," “good compensation," and “workplace is close to my residence." The overall evaluation of the level of thriving facilitation workplace reached almost to “mostly agree." For those participants who chose at least one motivator as their job retention options had significantly greater job satisfaction than those who chose only hygiene factors as their retention options. Therefore, Herzberg-s Two-Factor Theory of Motivation was proven valid in this study.

Investigation of Increasing the Heat Transfer from Flat Surfaces Using Boundary Layer Excitation

The present study is concerned with effect of exciting boundary layer on increase in heat transfer from flat surfaces. As any increase in heat transfer between a fluid inside a face and another one outside of it can cause an increase in some equipment's efficiency, so at this present we have tried to increase the wall's heat transfer coefficient by exciting the fluid boundary layer. By a collision between flow and the placed block at the fluid way, the flow pattern and the boundary layer stability will change. The flow way inside the channel is simulated as a 2&3-dimensional channel by Gambit TM software. With studying the achieved results by this simulation for the flow way inside the channel with a block coordinating with Fluent TM software, it's determined that the figure and dimensions of the exciter are too important for exciting the boundary layer so that any increase in block dimensions in vertical side against the flow and any reduction in its dimensions at the flow side can increase the average heat transfer coefficient from flat surface and increase the flow pressure loss. Using 2&3-dimensional analysis on exciting the flow at the flow way inside a channel by cylindrical block at the same time with the external flow, we came to this conclusion that the heat flux transferred from the surface, is increased considerably in terms of the condition without excitation. Also, the k-e turbulence model is used.

Biological Data Integration using SOA

Nowadays scientific data is inevitably digital and stored in a wide variety of formats in heterogeneous systems. Scientists need to access an integrated view of remote or local heterogeneous data sources with advanced data accessing, analyzing, and visualization tools. This research suggests the use of Service Oriented Architecture (SOA) to integrate biological data from different data sources. This work shows SOA will solve the problems that facing integration process and if the biologist scientists can access the biological data in easier way. There are several methods to implement SOA but web service is the most popular method. The Microsoft .Net Framework used to implement proposed architecture.

Segmental and Subsegmental Lung Vessel Segmentation in CTA Images

In this paper, a novel and fast algorithm for segmental and subsegmental lung vessel segmentation is introduced using Computed Tomography Angiography images. This process is quite important especially at the detection of pulmonary embolism, lung nodule, and interstitial lung disease. The applied method has been realized at five steps. At the first step, lung segmentation is achieved. At the second one, images are threshold and differences between the images are detected. At the third one, left and right lungs are gathered with the differences which are attained in the second step and Exact Lung Image (ELI) is achieved. At the fourth one, image, which is threshold for vessel, is gathered with the ELI. Lastly, identifying and segmentation of segmental and subsegmental lung vessel have been carried out thanks to image which is obtained in the fourth step. The performance of the applied method is found quite well for radiologists and it gives enough results to the surgeries medically.

Offline Signature Recognition using Radon Transform

In this work a new offline signature recognition system based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of the system several experiments are carried out. Offline signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.

Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification

In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.