Experimental Study on Machinability of Laser- Sintered Material in Ball End Milling

This paper presents an experimental investigation on the machinability of laser-sintered material using small ball end mill focusing on wear mechanisms. Laser-sintered material was produced by irradiating a laser beam on a layer of loose fine SCM-Ni-Cu powder. Bulk carbon steel JIS S55C was selected as a reference steel. The effects of powder consolidation mechanisms and unsintered powder on the tool life and wear mechanisms were carried out. Results indicated that tool life in cutting laser-sintered material is lower than that in cutting JIS S55C. Adhesion of the work material and chipping were the main wear mechanisms of the ball end mill in cutting laser-sintered material. Cutting with the unsintered powder surrounding the tool and laser-sintered material had caused major fracture on the cutting edge.

A Discrete Filtering Algorithm for Impulse Wave Parameter Estimation

This paper presents a new method for estimating the mean curve of impulse voltage waveforms that are recorded during impulse tests. In practice, these waveforms are distorted by noise, oscillations and overshoot. The problem is formulated as an estimation problem. Estimation of the current signal parameters is achieved using a fast and accurate technique. The method is based on discrete dynamic filtering algorithm (DDF). The main advantage of the proposed technique is its ability in producing the estimates in a very short time and at a very high degree of accuracy. The algorithm uses sets of digital samples of the recorded impulse waveform. The proposed technique has been tested using simulated data of practical waveforms. Effects of number of samples and data window size are studied. Results are reported and discussed.

Investigation of Genetic Epidemiology of Metabolic Compromises in ß Thalassemia Minor Mutation: Phenotypic Pleiotropy

Human genome is not only the evolutionary summation of all advantageous events, but also houses lesions of deleterious foot prints. A single gene mutation sometimes may express multiple consequences in numerous tissues and a linear relationship of the genotype and the phenotype may often be obscure. ß Thalassemia minor, a transfusion independent mild anaemia, coupled with environment among other factors may articulate into phenotypic pleotropy with Hypocholesterolemia, Vitamin D deficiency, Tissue hypoxia, Hyper-parathyroidism and Psychological alterations. Occurrence of Pancreatic insufficiency, resultant steatorrhoea, Vitamin-D (25-OH) deficiency (13.86 ngm/ml) with Hypocholesterolemia (85mg/dl) in a 30 years old male ß Thal-minor patient (Hemoglobin 11mg/dl with Fetal Hemoglobin 2.10%, Hb A2 4.60% and Hb Adult 84.80% and altered Hemogram) with increased Para thyroid hormone (62 pg/ml) & moderate Serum Ca+2 (9.5mg/ml) indicate towards a cascade of phenotypic pleotropy where the ß Thalassemia mutation ,be it in the 5’ cap site of the mRNA , differential splicing etc in heterozygous state is effecting several metabolic pathways. Compensatory extramedulary hematopoiesis may not coped up well with the stressful life style of the young individual and increased erythropoietic stress with high demand for cholesterol for RBC membrane synthesis may have resulted in Hypocholesterolemia.Oxidative stress and tissue hypoxia may have caused the pancreatic insufficiency, leading to Vitamin D deficiency. This may in turn have caused the secondary hyperparathyroidism to sustain serum Calcium level. Irritability and stress intolerance of the patient was a cumulative effect of the vicious cycle of metabolic compromises. From these findings we propose that the metabolic deficiencies in the ß Thalassemia mutations may be considered as the phenotypic display of the pleotropy to explain the genetic epidemiology. According to the recommendations from the NIH Workshop on Gene-Environment Interplay in Common Complex Diseases: Forging an Integrative Model, study design of observations should be informed by gene-environment hypotheses and results of a study (genetic diseases) should be published to inform future hypotheses. Variety of approaches is needed to capture data on all possible aspects, each of which is likely to contribute to the etiology of disease. Speakers also agreed that there is a need for development of new statistical methods and measurement tools to appraise information that may be missed out by conventional method where large sample size is needed to segregate considerable effect. A meta analytic cohort study in future may bring about significant insight on to the title comment.

Instability Problem of Turbo-Machines with Radial Distortion Problems

In the upstream we place a piece of ring and rotate it with 83Hz, 166Hz, 333Hz,and 666H to find the effect of the periodic distortion.In the experiment this type of the perturbation will not allow since the mechanical failure of any parts of the equipment in the upstream will destroy the blade system. This type of study will be only possible by CFD. We use two pumps NS32 (ENSAM) and three blades pump (Tamagawa Univ). The benchmark computations were performed without perturbation parts, and confirm the computational results well agreement in head-flow rate. We obtained the pressure fluctuation growth rate that is representing the global instability of the turbo-system. The fluctuating torque components were 0.01Nm(5000rpm), 0.1Nm(10000rmp), 0.04Nm(20000rmp), 0.15Nm( 40000rmp) respectively. Only for 10000rpm(166Hz) the output toque was random, and it implies that it creates unsteady flow by separations on the blades, and will reduce the pressure loss significantly

On the Fast Convergence of DD-LMS DFE Using a Good Strategy Initialization

In wireless communication system, a Decision Feedback Equalizer (DFE) to cancel the intersymbol interference (ISI) is required. In this paper, an exact convergence analysis of the (DFE) adapted by the Least Mean Square (LMS) algorithm during the training phase is derived by taking into account the finite alphabet context of data transmission. This allows us to determine the shortest training sequence that allows to reach a given Mean Square Error (MSE). With the intention of avoiding the problem of ill-convergence, the paper proposes an initialization strategy for the blind decision directed (DD) algorithm. This then yields a semi-blind DFE with high speed and good convergence.

A Simulator for Robot Navigation Algorithms

A robot simulator was developed to measure and investigate the performance of a robot navigation system based on the relative position of the robot with respect to random obstacles in any two dimensional environment. The presented simulator focuses on investigating the ability of a fuzzy-neural system for object avoidance. A navigation algorithm is proposed and used to allow random navigation of a robot among obstacles when the robot faces an obstacle in the environment. The main features of this simulator can be used for evaluating the performance of any system that can provide the position of the robot with respect to obstacles in the environment. This allows a robot developer to investigate and analyze the performance of a robot without implementing the physical robot.

Comparison of Different Neural Network Approaches for the Prediction of Kidney Dysfunction

This paper presents the prediction of kidney dysfunction using different neural network (NN) approaches. Self organization Maps (SOM), Probabilistic Neural Network (PNN) and Multi Layer Perceptron Neural Network (MLPNN) trained with Back Propagation Algorithm (BPA) are used in this study. Six hundred and sixty three sets of analytical laboratory tests have been collected from one of the private clinical laboratories in Baghdad. For each subject, Serum urea and Serum creatinin levels have been analyzed and tested by using clinical laboratory measurements. The collected urea and cretinine levels are then used as inputs to the three NN models in which the training process is done by different neural approaches. SOM which is a class of unsupervised network whereas PNN and BPNN are considered as class of supervised networks. These networks are used as a classifier to predict whether kidney is normal or it will have a dysfunction. The accuracy of prediction, sensitivity and specificity were found for each type of the proposed networks .We conclude that PNN gives faster and more accurate prediction of kidney dysfunction and it works as promising tool for predicting of routine kidney dysfunction from the clinical laboratory data.

A New Precautionary Method for Measurement and Improvement the Data Quality

the data quality is a kind of complex and unstructured concept, which is concerned by information systems managers. The reason of this attention is the high amount of Expenses for maintenance and cleaning of the inefficient data. Such a data more than its expenses of lack of quality, cause wrong statistics, analysis and decisions in organizations. Therefor the managers intend to improve the quality of their information systems' data. One of the basic subjects of quality improvement is the evaluation of the amount of it. In this paper, we present a precautionary method, which with its application the data of information systems would have a better quality. Our method would cover different dimensions of data quality; therefor it has necessary integrity. The presented method has tested on three dimensions of accuracy, value-added and believability and the results confirm the improvement and integrity of this method.

Efficient Moment Frame Structure

A different concept for designing and detailing of reinforced concrete precast frame structures is analyzed in this paper. The new detailing of the joints derives from the special hybrid moment frame joints. The special reinforcements of this alternative detailing, named modified special hybrid joint, are bondless with respect to both column and beams. Full scale tests were performed on a plan model, which represents a part of 5 story structure, cropped in the middle of the beams and columns spans. Theoretical approach was developed, based on testing results on twice repaired model, subjected to lateral seismic type loading. Discussion regarding the modified special hybrid joint behavior and further on widening research needed concludes the presentation.

Color Image Segmentation Using Competitive and Cooperative Learning Approach

Color image segmentation can be considered as a cluster procedure in feature space. k-means and its adaptive version, i.e. competitive learning approach are powerful tools for data clustering. But k-means and competitive learning suffer from several drawbacks such as dead-unit problem and need to pre-specify number of cluster. In this paper, we will explore to use competitive and cooperative learning approach to perform color image segmentation. In competitive and cooperative learning approach, seed points not only compete each other, but also the winner will dynamically select several nearest competitors to form a cooperative team to adapt to the input together, finally it can automatically select the correct number of cluster and avoid the dead-units problem. Experimental results show that CCL can obtain better segmentation result.

Entrepreneurship, Innovation, Incubator and Economic Development: A Case Study

The objective of this paper is twofold: (1) discuss and analyze the successful case studies worldwide, and (2) identify the similarities and differences of case studies worldwide. Design methodology/approach: The nature of this research is mainly method qualitative (multi-case studies, literature review). This investigation uses ten case studies, and the data was mainly collected and organizational documents from the international countries. Finding: The finding of this research can help incubator manager, policy maker and government parties for successful implementation. Originality/value: This paper contributes to the current literate review on the best practices worldwide. Additionally, it presents future perspective for academicians and practitioners.

Application of Extreme Learning Machine Method for Time Series Analysis

In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.

An Optimized Multi-block Method for Turbulent Flows

A major part of the flow field involves no complicated turbulent behavior in many turbulent flows. In this research work, in order to reduce required memory and CPU time, the flow field was decomposed into several blocks, each block including its special turbulence. A two dimensional backward facing step was considered here. Four combinations of the Prandtl mixing length and standard k- E models were implemented as well. Computer memory and CPU time consumption in addition to numerical convergence and accuracy of the obtained results were mainly investigated. Observations showed that, a suitable combination of turbulence models in different blocks led to the results with the same accuracy as the high order turbulence model for all of the blocks, in addition to the reductions in memory and CPU time consumption.

Single-Camera EKF-vSLAM

This paper presents an Extended Kaman Filter implementation of a single-camera Visual Simultaneous Localization and Mapping algorithm, a novel algorithm for simultaneous localization and mapping problem widely studied in mobile robotics field. The algorithm is vision and odometry-based, The odometry data is incremental, and therefore it will accumulate error over time, since the robot may slip or may be lifted, consequently if the odometry is used alone we can not accurately estimate the robot position, in this paper we show that a combination of odometry and visual landmark via the extended Kalman filter can improve the robot position estimate. We use a Pioneer II robot and motorized pan tilt camera models to implement the algorithm.

Output Regulation of Perturbed Nonlinear Systems by Nested Sliding Mode Control

In this paper, we consider nested sliding mode control of SISO nonlinear systems, perturbed by bounded matched and unmatched uncertainties. The systems are assumed to be in strict-feedback form. A step wise procedure is introduced to obtain the controller. In each step, a continuous sliding mode controller is designed as virtual control law. Then the next step sliding surface is defined by using this virtual controller. These sliding surfaces are selected as nonlinear static functions of the system states. Finally in the last step, smooth static state feedback control law is determined such that the output reaches the desired set-point while the system is forced arbitrary close to the intersection of sliding surfaces and the states remain bounded.

Impregnation of Cupper into Kanuma Volcanic Ash Soil to Improve Mercury Sorption Capacity

The present study attempted to improve the Mercury (Hg) sorption capacity of kanuma volcanic ash soil (KVAS) by impregnating the cupper (Cu). Impregnation was executed by 1 and 5% Cu powder and sorption characterization of optimum Hg removing Cu impregnated KVAS was performed under different operational conditions, contact time, solution pH, sorbent dosage and Hg concentration using the batch operation studies. The 1% Cu impregnated KVAS pronounced optimum improvement (79%) in removing Hg from water compare to control. The present investigation determined the equilibrium state of maximum Hg adsorption at 6 h contact period. The adsorption revealed a pH dependent response and pH 3.5 showed maximum sorption capacity of Hg. Freundlich isotherm model is well fitted with the experimental data than that of Langmuir isotherm. It can be concluded that the Cu impregnation improves the Hg sorption capacity of KVAS and 1% Cu impregnated KVAS could be employed as cost-effective adsorbent media for treating Hg contaminated water.

Recycling Organic Waste in Suan Sunandha Rajabhat University as Compost

This research aimed to study on the potential of recycling organic waste in Suan Sunandha Rajabhat University as compost. In doing so, the composition of solid waste generated in the campus was investigated while physical and chemical properties of organic waste were analyzed in order to evaluate the portion of waste suitable for recycling as compost. As a result of the study, it was found that (1) the amount of organic waste was averaged at 299.8 kg/day in which mixed food wastes had the highest amount of 191.9 kg/day followed by mixed leave & yard wastes and mixed fruit & vegetable wastes at the amount of 66.3 and 41.6 kg/day respectively; (2) physical and chemical properties of organic waste in terms of moisture content was between 69.54 to 78.15%, major elements for plant as N, P and K were 0.14 to 0.17%, 0.46 to 0.52% and 0.16 to 0.18% respectively, and carbon/nitrogen ratio (C/N) was about 15:1 to 17.5:1; (3) recycling organic waste as compost was designed by aerobic decomposition using mixed food wastes : mixed leave & yard wastes : mixed fruit & vegetable wastes at the portion of 3:2:1 by weight in accordance with the potential of their amounts and their physical and chemical properties.

The Effect of Hylocereus polyrhizus and Hylocereus undatus on Physicochemical, Proteolysis, and Antioxidant Activity in Yogurt

Yogurt is a coagulated milk product obtained from the lactic acid fermentation by the action of Lactobacillus bulgaricus and Streptococcus thermophilus. The additions of fruits into milk may enhance the taste and the therapeutical values of milk products. However fruits also may change the fermentation behaviour. In this present study, the changes in physicochemical, the peptide concentration, total phenolics content and the antioxidant potential of yogurt upon the addition of Hylocereus polyrhizus and Hylocereus undatus (white and red dragon fruit) were investigated. Fruits enriched yogurt (10%, 20%, 30% w/w) were prepared and the pH, TTA, syneresis measurement, peptide concentration, total phenolics content and DPPH antioxidant inhibition percentage were determined. Milk fermentation rate was enhanced in red dragon fruit yogurt for all doses (-0.3606 - -0.4126 pH/h) while only white dragon fruit yogurt with 20% and 30% (w/w) composition showed increment in fermentation rate (-0.3471 - -0.3609 pH/h) compared to plain yogurt (-0.3369pH/h). All dragon fruit enriched yogurts generally showed lower pH readings (pH 3.95 - 4.03) compared to plain yogurt (pH 4.05). Both fruit yogurts showed a higher lactic acid percentage (1.14-1.23%) compared to plain yogurt (1.08%). Significantly higher syneresis percentage (57.19 - 70.32%) compared to plain yogurt (52.93%) were seen in all fruit enriched yogurts. The antioxidant activity of plain yogurt (19.16%) was enhanced by the presence of white and red dragon fruit (24.97- 45.74%). All fruit enriched yogurt showed an increment in total phenolic content (36.44 - 64.43mg/ml) compared to plain yogurt (20.25mg/ml). However, the addition of white and red dragon fruit did not enhance the proteolysis of milk during fermentation. Therefore, it could be concluded that the addition of white and red dragon fruit into yogurt enhanced the milk fermentation rate, lactic acid content, syneresis percentage, antioxidant activity, and total phenolics content in yogurt.

Serum Nitric Oxide and Sialic Acid: Possible Biochemical Markers for Progression of Diabetic Nephropathy

This study was designed to investigate the role of serum nitric oxide and sialic acid in the development of diabetic nephropathy as disease marker. Total 210 diabetic patients (age and sex matched) were selected followed by informed consent and divided into four groups (70 each) as I: control; II: diabetic; III: diabetic hypertensive; IV: diabetic nephropathy. The blood samples of all subjects were collected and analyzed for serum nitric oxide, sialic acid, fasting blood glucose, serum urea, creatinine, HbA1c and GFR. The BMI, systolic and diastolic blood pressures, blood glucose, HbA1c and serum sialic acid levels were high (p

Transfer Function of Piezoelectric Material

The study of piezoelectric material in the past was in T-Domain form; however, no one has studied piezoelectric material in the S-Domain form. This paper will present the piezoelectric material in the transfer function or S-Domain model. S-Domain is a well known mathematical model, used for analyzing the stability of the material and determining the stability limits. By using S-Domain in testing stability of piezoelectric material, it will provide a new tool for the scientific world to study this material in various forms.