Search results for: correlation weighting.
216 Development of a Neural Network based Algorithm for Multi-Scale Roughness Parameters and Soil Moisture Retrieval
Authors: L. Bennaceur Farah, I. R. Farah, R. Bennaceur, Z. Belhadj, M. R. Boussema
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The overall objective of this paper is to retrieve soil surfaces parameters namely, roughness and soil moisture related to the dielectric constant by inverting the radar backscattered signal from natural soil surfaces. Because the classical description of roughness using statistical parameters like the correlation length doesn't lead to satisfactory results to predict radar backscattering, we used a multi-scale roughness description using the wavelet transform and the Mallat algorithm. In this description, the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each having a spatial scale. A second step in this study consisted in adapting a direct model simulating radar backscattering namely the small perturbation model to this multi-scale surface description. We investigated the impact of this description on radar backscattering through a sensitivity analysis of backscattering coefficient to the multi-scale roughness parameters. To perform the inversion of the small perturbation multi-scale scattering model (MLS SPM) we used a multi-layer neural network architecture trained by backpropagation learning rule. The inversion leads to satisfactory results with a relative uncertainty of 8%.Keywords: Remote sensing, rough surfaces, inverse problems, SAR, radar scattering, Neural networks and Fractals.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1583215 Investigation the Difference of Several Hormones Correlated to Reproduction between Infertile and Fertile Dairy Cows
Authors: Ali M. Mutlag, Yang Zhiqiang, Meng Jiaren, Zhang Jingyan, Li Jianxi
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The object of this study was to investigate several hormones correlated to the reproduction and inhibin A, inhibin B and NO levels in the infertile dairy cows as attempt to illustrate the physiological causes of dairy cows infertility.
40 Holstein cow (21 infertile and 19 fertile) were used at estrous phase of the cycle, Hormones FSH, LH, E2, Testosterone, were measured using ELISA method. inhibin A and B also estimated by ELISA method, Nitric oxide was measured by Greiss reagent method.
The results showed different concentrations of the hormone in which FSH illustrated significantly higher concentration in the infertile cows than fertile cows (P<0.05). LH and E2 showed significant decrease in the infertile cows than the fertile cows (P<0.05), no significant difference appeared in testosterone concentrations in the fertile cows and infertile cows (P>0.05). The both inhibins A and B showed significant P<0.05 decrease concentrations in the infertile cows also NO showed clearly significant decrease P<0.05 in the infertile cows.
In conclusion, the present study approved the poorly ovarian activities and reproduction disturbance of infertile cows in spite of trigger estrous signs, the study confirmed a positive correlation between inhibins and NO to regulate the ovarian physiology. These inhibins represent effective markers of dairy cow infertility.
Keywords: Cows, Inhibin (A, B), Infertility, Nitric oxide (NO).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1907214 EFL Teachers’ Metacognitive Awareness as a Predictor of Their Professional Success
Authors: Saeedeh Shafiee Nahrkhalaji
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Metacognitive knowledge increases EFL students’ ability to be successful learners. Although this relationship has been investigated by a number of scholars, EFL teachers’ explicit awareness of their cognitive knowledge has not been sufficiently explored. The aim of this study was to examine the role of EFL teachers’ metacognitive knowledge in their pedagogical performance. Furthermore, the role played by years of their academic education and teaching experience was also studied. Fifty female EFL teachers were selected. They completed Metacognitive Awareness Inventory (MAI) that assessed six components of metacognition including procedural knowledge, declarative knowledge, conditional knowledge, planning, evaluating, and management strategies. Near the end of the academic semester, the students of each class filled in ‘the Language Teacher Characteristics Questionnaire’ to evaluate their teachers’ pedagogical performance. Four elements of MAI, declarative knowledge, planning, evaluating, and management strategies were found to be significantly correlated with EFL teachers’ pedagogical success. Significant correlation was also established between metacognitive knowledge and EFL teachers’ years of academic education and teaching experience. The findings obtained from this research have contributing implication for EFL teacher educators. The discussion concludes by setting out directions for future research.
Keywords: Metacognotive Knowledge, Pedagogical Performance, Language Teacher Characteristics Questionnaire, Metacognitive Awareness Inventory.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2698213 Evaluation of Deformable Boundary Condition Using Finite Element Method and Impact Test for Steel Tubes
Authors: Abed Ahmed, Mehrdad Asadi, Jennifer Martay
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Stainless steel pipelines are crucial components to transportation and storage in the oil and gas industry. However, the rise of random attacks and vandalism on these pipes for their valuable transport has led to more security and protection for incoming surface impacts. These surface impacts can lead to large global deformations of the pipe and place the pipe under strain, causing the eventual failure of the pipeline. Therefore, understanding how these surface impact loads affect the pipes is vital to improving the pipes’ security and protection. In this study, experimental test and finite element analysis (FEA) have been carried out on EN3B stainless steel specimens to study the impact behaviour. Low velocity impact tests at 9 m/s with 16 kg dome impactor was used to simulate for high momentum impact for localised failure. FEA models of clamped and deformable boundaries were modelled to study the effect of the boundaries on the pipes impact behaviour on its impact resistance, using experimental and FEA approach. Comparison of experimental and FE simulation shows good correlation to the deformable boundaries in order to validate the robustness of the FE model to be implemented in pipe models with complex anisotropic structure.
Keywords: Dynamic impact, deformable boundary conditions, finite element modeling, FEM, finite element, FE, LS-DYNA, Stainless steel pipe.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 682212 Interannual Variations in Snowfall and Continuous Snow Cover Duration in Pelso, Central Finland, Linked to Teleconnection Patterns, 1944-2010
Authors: M. Irannezhad, E. H. N. Gashti, S. Mohammadighavam, M. Zarrini, B. Kløve
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Climate warming would increase rainfall by shifting precipitation falling form from snow to rain, and would accelerate snow cover disappearing by increasing snowpack. Using temperature and precipitation data in the temperature-index snowmelt model, we evaluated variability of snowfall and continuous snow cover duration (CSCD) during 1944-2010 over Pelso, central Finland. Mann- Kendall non-parametric test determined that annual precipitation increased by 2.69 (mm/year, p<0.05) during the study period, but no clear trend in annual temperature. Both annual rainfall and snowfall increased by 1.67 and 0.78 (mm/year, p<0.05), respectively. CSCD was generally about 205 days from 14 October to 6 May. No clear trend was found in CSCD over Pelso. Spearman’s rank correlation showed most significant relationships of annual snowfall with the East Atlantic (EA) pattern, and CSCD with the East Atlantic/West Russia (EA/WR) pattern. Increased precipitation with no warming temperature caused the rainfall and snowfall to increase, while no effects on CSCD.
Keywords: Variations, snowfall, snow cover duration, temperature-index snowmelt model, teleconnection patterns.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1904211 Investigation of Fire Damaged Concrete Using Nonlinear Resonance Vibration Method
Authors: Kang-Gyu Park, Sun-Jong Park, Hong Jae Yim, Hyo-Gyung Kwak
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This paper attempts to evaluate the effect of fire damage on concrete by using nonlinear resonance vibration method, one of the nonlinear nondestructive method. Concrete exhibits not only nonlinear stress-strain relation but also hysteresis and discrete memory effect which are contained in consolidated materials. Hysteretic materials typically show the linear resonance frequency shift. Also, the shift of resonance frequency is changed according to the degree of micro damage. The degree of the shift can be obtained through nonlinear resonance vibration method. Five exposure scenarios were considered in order to make different internal micro damage. Also, the effect of post-fire-curing on fire-damaged concrete was taken into account to conform the change in internal damage. Hysteretic nonlinearity parameter was obtained by amplitudedependent resonance frequency shift after specific curing periods. In addition, splitting tensile strength was measured on each sample to characterize the variation of residual strength. Then, a correlation between the hysteretic nonlinearity parameter and residual strength was proposed from each test result.
Keywords: Fire damaged concrete, nonlinear resonance vibration method, nonlinearity parameter, post-fire-curing, splitting tensile strength.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2112210 A Study on Improving the Flow Capacity of the Valves
Authors: A. G. Pradeep, Gorantla Giridhar Kumar, Vijay Turaga, Vinod Srinivasa
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The major problem in the flow control valve is of lower Flow Capacity (Cv) which will reduce overall efficiency of flow circuit. Designers are continuously working to improve the Cv of the valve, but they need to validate the design ideas they have regarding the improvement of Cv. Traditional method of prototype and testing take a lot of time, that is where CFD comes into picture with very quick and accurate validation along with the visualization which is not possible with traditional testing method. We have developed a method to predict Cv value using CFD analysis by iterating on various Boundary conditions, solver settings and by carrying out grid convergence studies to establish correlation between the CFD model and Test data. The present study investigates 3 different ideas put forward by the designers for improving the flow capacity of the valves like reducing the cage thickness, changing the port position, and using the parabolic plug to guide the flow. Using CFD, we analyzed all design changes using the established methodology that we developed. We were able to evaluate the effect of these design changes on the Valve Cv. We optimized the wetted surface of the valve further by suggesting the design modification to the lower part of the valve to make the flow more streamlined. We could find that changing cage thickness and port position has little impact on the valve Cv. Combination of optimized wetted surface and introduction of parabolic plug improved the Cv of the valve significantly.
Keywords: Flow control valves, flow capacity, CFD simulations, design validation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 417209 Novel Hybrid Method for Gene Selection and Cancer Prediction
Authors: Liping Jing, Michael K. Ng, Tieyong Zeng
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Microarray data profiles gene expression on a whole genome scale, therefore, it provides a good way to study associations between gene expression and occurrence or progression of cancer. More and more researchers realized that microarray data is helpful to predict cancer sample. However, the high dimension of gene expressions is much larger than the sample size, which makes this task very difficult. Therefore, how to identify the significant genes causing cancer becomes emergency and also a hot and hard research topic. Many feature selection algorithms have been proposed in the past focusing on improving cancer predictive accuracy at the expense of ignoring the correlations between the features. In this work, a novel framework (named by SGS) is presented for stable gene selection and efficient cancer prediction . The proposed framework first performs clustering algorithm to find the gene groups where genes in each group have higher correlation coefficient, and then selects the significant genes in each group with Bayesian Lasso and important gene groups with group Lasso, and finally builds prediction model based on the shrinkage gene space with efficient classification algorithm (such as, SVM, 1NN, Regression and etc.). Experiment results on real world data show that the proposed framework often outperforms the existing feature selection and prediction methods, say SAM, IG and Lasso-type prediction model.Keywords: Gene Selection, Cancer Prediction, Lasso, Clustering, Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2035208 The Appropriate Time Required for Newborn Calf Camel to Get Optimal Amount of Colostrums Immunoglobulin (IgG) with Relation to Levels of Cortisol and Thyroxin
Authors: Amina M. Bishr, Ahmed B. Magdub, Abdul-Baset R. Abuzweda
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A major challenge in camel productivity is the high mortality rate of camel calves in the early stage due to the lack of colostrums. This study investigates the time required for the calves to obtain the optimum amount of the immunoglobulin (IgG). Eleven pregnant female camels (Camelus Dromedarus) were selected randomly and variant in age and gestation. After delivery, 7 calves were obtained and used for this investigation. Colostrum samples were collected from mothers immediately after parturition. Blood samples were obtained from the calves as follow: 0 day (before suckling), 24, 48, 72, 96, 120 and 144 hours, 2nd, 3rd, and 4th weeks post suckling. Blood serum and colostrums whey were separated and used to determine IgG concentration, total protein and concentration of Cortisol and Thyroxin. The results showed high levels of IgG in camel colostrums (328.8 ± 4.5 mg / ml). The IgG concentration in serum of calves was the highest within 1st 24 h after suckling (140.75 mg /ml), and then declined gradually reached lower level at 144 h (41.97 mg / ml). The average turnover rate (t 1/2) of serum IgG in the all cases was 3.22 days. The turnover of ranged from 2.56 days for calves have values of IgG more than average and 7.7 days for those with values below average. In spite of very high levels of thyroxin in sera of new born the results showed no correlation between cortisol and thyroxin with IgG levels.Keywords: Camel, cortisol, IgG, thyroxin, turn-over rate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2010207 Larval Occurrence and Climatic Factors Affecting DHF Incidence in Samui Islands, Thailand
Authors: S. Wongkoon, M. Jaroensutasinee, K. Jaroensutasinee, W. Preechaporn, S. Chumkiew
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This study investigated the number of Aedes larvae, the key breeding sites of Aedes sp., and the relationship between climatic factors and the incidence of DHF in Samui Islands. We conducted our questionnaire and larval surveys from randomly selected 105 households in Samui Islands in July-September 2006. Pearson-s correlation coefficient was used to explore the primary association between the DHF incidence and all climatic factors. Multiple stepwise regression technique was then used to fit the statistical model. The results showed that the positive indoor containers were small jars, cement tanks, and plastic tanks. The positive outdoor containers were small jars, cement tanks, plastic tanks, used cans, tires, plastic bottles, discarded objects, pot saucers, plant pots, and areca husks. All Ae. albopictus larval indices (i.e., CI, HI, and BI) were higher than Ae. aegypti larval indices in this area. These larval indices were higher than WHO standard. This indicated a high risk of DHF transmission at Samui Islands. The multiple stepwise regression model was y = –288.80 + 11.024xmean temp. The mean temperature was positively associated with the DHF incidence in this area.Keywords: Dengue vectors, Aedes aegypti, Aedes albopictus, Container Index, House Index, Breteau Index, Aedes indices, Climatic factors, Temperature.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1746206 Nonlinear Modeling and Analysis of AAC infilled Sandwich Panels for out of Plane Loads
Authors: Al-Kashif M., Abdel-Mooty M., Fahmy E., Abou Zeid M., Haroun M.
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Sandwich panels are widely used in the construction industry for their ease of assembly, light weight and efficient thermal performance. They are composed of two RC thin outer layers separated by an insulating inner layer. In this research the inner insulating layer is made of lightweight Autoclaved Aerated Concrete (AAC) blocks which has good thermal insulation properties and yet possess reasonable mechanical strength. The shear strength of the AAC infill is relied upon to replace the traditionally used insulating foam and to provide the shear capacity of the panel. A comprehensive experimental program was conducted on full scale sandwich panels subjected to bending. In this paper, detailed numerical modeling of the tested sandwich panels is reported. Nonlinear 3-D finite element modeling of the composite action of the sandwich panel is developed using ANSYS. Solid elements with different crashing and cracking capabilities and different constitutive laws were selected for the concrete and the AAC. Contact interface elements are used in this research to adequately model the shear transfer at the interface between the different layers. The numerical results showed good correlation with the experimental ones indicating the adequacy of the model in estimating the loading capacity of panels.Keywords: Autoclaved Aerated Concrete, Concrete Sandwich Panels, Finite Element Modeling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3071205 Computer Software Applicable in Rehabilitation, Cardiology and Molecular Biology
Authors: P. Kowalska, P. Gabka, K. Kamieniarz, M. Kamieniarz, W. Stryla, P. Guzik, T. Krauze
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We have developed a computer program consisting of 6 subtests assessing the children hand dexterity applicable in the rehabilitation medicine. We have carried out a normative study on a representative sample of 285 children aged from 7 to 15 (mean age 11.3) and we have proposed clinical standards for three age groups (7-9, 9-11, 12-15 years). We have shown statistical significance of differences among the corresponding mean values of the task time completion. We have also found a strong correlation between the task time completion and the age of the subjects, as well as we have performed the test-retest reliability checks in the sample of 84 children, giving the high values of the Pearson coefficients for the dominant and non-dominant hand in the range 0.74Keywords: Biomedical data base processing, Computer software, Hand dexterity, Heart rate and blood pressure variability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1462204 Motion Prediction and Motion Vector Cost Reduction during Fast Block Motion Estimation in MCTF
Authors: Karunakar A K, Manohara Pai M M
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In 3D-wavelet video coding framework temporal filtering is done along the trajectory of motion using Motion Compensated Temporal Filtering (MCTF). Hence computationally efficient motion estimation technique is the need of MCTF. In this paper a predictive technique is proposed in order to reduce the computational complexity of the MCTF framework, by exploiting the high correlation among the frames in a Group Of Picture (GOP). The proposed technique applies coarse and fine searches of any fast block based motion estimation, only to the first pair of frames in a GOP. The generated motion vectors are supplied to the next consecutive frames, even to subsequent temporal levels and only fine search is carried out around those predicted motion vectors. Hence coarse search is skipped for all the motion estimation in a GOP except for the first pair of frames. The technique has been tested for different fast block based motion estimation algorithms over different standard test sequences using MC-EZBC, a state-of-the-art scalable video coder. The simulation result reveals substantial reduction (i.e. 20.75% to 38.24%) in the number of search points during motion estimation, without compromising the quality of the reconstructed video compared to non-predictive techniques. Since the motion vectors of all the pair of frames in a GOP except the first pair will have value ±1 around the motion vectors of the previous pair of frames, the number of bits required for motion vectors is also reduced by 50%.Keywords: Motion Compensated Temporal Filtering, predictivemotion estimation, lifted wavelet transform, motion vector
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1608203 Performance Analysis of Evolutionary ANN for Output Prediction of a Grid-Connected Photovoltaic System
Authors: S.I Sulaiman, T.K Abdul Rahman, I. Musirin, S. Shaari
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This paper presents performance analysis of the Evolutionary Programming-Artificial Neural Network (EPANN) based technique to optimize the architecture and training parameters of a one-hidden layer feedforward ANN model for the prediction of energy output from a grid connected photovoltaic system. The ANN utilizes solar radiation and ambient temperature as its inputs while the output is the total watt-hour energy produced from the grid-connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for the training. The EPANN model is tested using two types of transfer function for the hidden layer, namely the tangent sigmoid and logarithmic sigmoid. The best transfer function, neural topology and learning parameters were selected based on the highest regression performance obtained during the ANN training and testing process. It is observed that the best transfer function configuration for the prediction model is [logarithmic sigmoid, purely linear].Keywords: Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1894202 Two Phase Frictional Pressure Drop of Carbon Dioxide in Horizontal Micro Tubes
Authors: M. Tarawneh
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Two-phase frictional pressure drop data were obtained for condensation of carbon dioxide in single horizontal micro tube of inner diameter ranged from 0.6 mm up to 1.6 mm over mass flow rates from 2.5*10-5 to 17*10-5 kg/s and vapor qualities from 0.0 to 1.0. The inlet condensing pressure is changed from 33.5 to 45 bars. The saturation temperature ranged from -1.5 oC up to 10 oC. These data have then been compared against three (two-phase) frictional pressure drop prediction methods. The first method is by Muller-Steinhagen and Heck (Muller-Steinhagen H, Heck K. A simple friction pressure drop correlation for two-phase flow in pipes. Chem. Eng. Process 1986;20:297–308) and that by Gronnerud R. Investigation of liquid hold-up, flow-resistance and heat transfer in circulation type evaporators, part IV: two-phase flow resistance in boiling refrigerants, Annexe 1972. Then the method used by FriedelL. Improved friction pressures drop in horizontal and vertical two-phase pipe flow. European Two-Phase Flow Group Meeting, Paper E2; 1979 June, Ispra, Italy. The methods are used by M.B Ould Didi et al (2001) “Prediction of two-phase pressure gradients of refrigerant in horizontal tubes". Int.J.of Refrigeration 25(2002) 935- 947. The best available method for annular flow was that of Muller- Steinhagen and Heck. It was observed that the peak in the two-phase frictional pressure gradient is at high vapor qualities.Keywords: Two-phase flow, frictional pressure drop, horizontalmicro tube, carbon dioxide, condensers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3354201 Brain Image Segmentation Using Conditional Random Field Based On Modified Artificial Bee Colony Optimization Algorithm
Authors: B. Thiagarajan, R. Bremananth
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Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different characteristics and treatments. Brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Locating the tumor within MR (magnetic resonance) image of brain is integral part of the treatment of brain tumor. This segmentation task requires classification of each voxel as either tumor or non-tumor, based on the description of the voxel under consideration. Many studies are going on in the medical field using Markov Random Fields (MRF) in segmentation of MR images. Even though the segmentation process is better, computing the probability and estimation of parameters is difficult. In order to overcome the aforementioned issues, Conditional Random Field (CRF) is used in this paper for segmentation, along with the modified artificial bee colony optimization and modified fuzzy possibility c-means (MFPCM) algorithm. This work is mainly focused to reduce the computational complexities, which are found in existing methods and aimed at getting higher accuracy. The efficiency of this work is evaluated using the parameters such as region non-uniformity, correlation and computation time. The experimental results are compared with the existing methods such as MRF with improved Genetic Algorithm (GA) and MRF-Artificial Bee Colony (MRF-ABC) algorithm.
Keywords: Conditional random field, Magnetic resonance, Markov random field, Modified artificial bee colony.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2937200 Prediction of the Lateral Bearing Capacity of Short Piles in Clayey Soils Using Imperialist Competitive Algorithm-Based Artificial Neural Networks
Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian
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Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.
Keywords: Lateral bearing capacity, short pile, clayey soil, artificial neural network, Imperialist competition algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 925199 General Regression Neural Network and Back Propagation Neural Network Modeling for Predicting Radial Overcut in EDM: A Comparative Study
Authors: Raja Das, M. K. Pradhan
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This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.
Keywords: Electrical-discharge machining, General Regression Neural Network, Back-propagation Neural Network, Radial Overcut.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3100198 A Multiple Linear Regression Model to Predict the Price of Cement in Nigeria
Authors: Kenneth M. Oba
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This study investigated factors affecting the price of cement in Nigeria, and developed a mathematical model that can predict future cement prices. Cement is key in the Nigerian construction industry. The changes in price caused by certain factors could affect economic and infrastructural development; hence there is need for proper proactive planning. Secondary data were collected from published information on cement between 2014 and 2019. In addition, questionnaires were sent to some domestic cement retailers in Port Harcourt in Nigeria, to obtain the actual prices of cement between the same periods. The study revealed that the most critical factors affecting the price of cement in Nigeria are inflation rate, population growth rate, and Gross Domestic Product (GDP) growth rate. With the use of data from United Nations, International Monetary Fund, and Central Bank of Nigeria databases, amongst others, a Multiple Linear Regression model was formulated. The model was used to predict the price of cement for 2020-2025. The model was then tested with 95% confidence level, using a two-tailed t-test and an F-test, resulting in an R2 of 0.8428 and R2 (adj.) of 0.6069. The results of the tests and the correlation factors confirm the model to be fit and adequate. This study will equip researchers and stakeholders in the construction industry with information for planning, monitoring, and management of present and future construction projects that involve the use of cement.
Keywords: Cement price, multiple linear regression model, Nigerian Construction Industry, price prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 778197 Stature Prediction Model Based On Hand Anthropometry
Authors: Arunesh Chandra, Pankaj Chandna, Surinder Deswal, Rajesh Kumar Mishra, Rajender Kumar
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The arm length, hand length, hand breadth and middle finger length of 1540 right-handed industrial workers of Haryana state was used to assess the relationship between the upper limb dimensions and stature. Initially, the data were analyzed using basic univariate analysis and independent t-tests; then simple and multiple linear regression models were used to estimate stature using SPSS (version 17). There was a positive correlation between upper limb measurements (hand length, hand breadth, arm length and middle finger length) and stature (p < 0.01), which was highest for hand length. The accuracy of stature prediction ranged from ± 54.897 mm to ± 58.307 mm. The use of multiple regression equations gave better results than simple regression equations. This study provides new forensic standards for stature estimation from the upper limb measurements of male industrial workers of Haryana (India). The results of this research indicate that stature can be determined using hand dimensions with accuracy, when only upper limb is available due to any reasons likewise explosions, train/plane crashes, mutilated bodies, etc. The regression formula derived in this study will be useful for anatomists, archaeologists, anthropologists, design engineers and forensic scientists for fairly prediction of stature using regression equations.
Keywords: Anthropometric dimensions, Forensic identification, Industrial workers, Stature prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2940196 Towards a Broader Understanding of Journal Impact: Measuring Relationships between Journal Characteristics and Scholarly Impact
Authors: X. Gu, K. L. Blackmore
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The impact factor was introduced to measure the quality of journals. Various impact measures exist from multiple bibliographic databases. In this research, we aim to provide a broader understanding of the relationship between scholarly impact and other characteristics of academic journals. Data used for this research were collected from Ulrich’s Periodicals Directory (Ulrichs), Cabell’s (Cabells), and SCImago Journal & Country Rank (SJR) from 1999 to 2015. A master journal dataset was consolidated via Journal Title and ISSN. We adopted a two-step analysis process to study the quantitative relationships between scholarly impact and other journal characteristics. Firstly, we conducted a correlation analysis over the data attributes, with results indicating that there are no correlations between any of the identified journal characteristics. Secondly, we examined the quantitative relationship between scholarly impact and other characteristics using quartile analysis. The results show interesting patterns, including some expected and others less anticipated. Results show that higher quartile journals publish more in both frequency and quantity, and charge more for subscription cost. Top quartile journals also have the lowest acceptance rates. Non-English journals are more likely to be categorized in lower quartiles, which are more likely to stop publishing than higher quartiles. Future work is suggested, which includes analysis of the relationship between scholars and their publications, based on the quartile ranking of journals in which they publish.
Keywords: Academic journal, acceptance rate, impact factor, journal characteristics.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 973195 Designing Social Care Policies in the Long Term: A Study Using Regression, Clustering and Backpropagation Neural Nets
Authors: Sotirios Raptis
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Linking social needs to social classes using different criteria may lead to social services misuse. The paper discusses using ML and Neural Networks (NNs) in linking public services in Scotland in the long term and advocates, this can result in a reduction of the services cost connecting resources needed in groups for similar services. The paper combines typical regression models with clustering and cross-correlation as complementary constituents to predict the demand. Insurance companies and public policymakers can pack linked services such as those offered to the elderly or to low-income people in the longer term. The work is based on public data from 22 services offered by Public Health Services (PHS) Scotland and from the Scottish Government (SG) from 1981 to 2019 that are broken into 110 years series called factors and uses Linear Regression (LR), Autoregression (ARMA) and 3 types of back-propagation (BP) Neural Networks (BPNN) to link them under specific conditions. Relationships found were between smoking related healthcare provision, mental health-related health services, and epidemiological weight in Primary 1(Education) Body Mass Index (BMI) in children. Primary component analysis (PCA) found 11 significant factors while C-Means (CM) clustering gave 5 major factors clusters.
Keywords: Probability, cohorts, data frames, services, prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 438194 A Study on Learning Styles and Academic Performance in Relation with Kinesthetic, Verbal and Visual Intelligences
Authors: Salina Budin, Nor Liawati Abu Othman, Shaira Ismail
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This study attempts to determine kinesthetic, verbal and visual intelligences among mechanical engineering undergraduate students and explores any probable relation with students’ learning styles and academic performance. The questionnaire used in this study is based on Howard Gardner’s multiple intelligences theory comprising of five elements of learning style; environmental, sociological, emotional, physiological and psychological. Questionnaires are distributed amongst undergraduates in the Faculty of Mechanical Engineering. Additional questions on students’ perception of learning styles and their academic performance are included in the questionnaire. The results show that one third of the students are strongly dominant in the kinesthetic intelligent (33%), followed by a combination of kinesthetic and visual intelligences (29%) and 21% are strongly dominant in all three types of intelligences. There is a statistically significant correlation between kinesthetic, verbal and visual intelligences and students learning styles and academic performances. The ANOVA analysis supports that there is a significant relationship between academic performances and level of kinesthetic, verbal and visual intelligences. In addition, it has also proven a remarkable relationship between academic performances and kinesthetic, verbal and visual learning styles amongst the male and female students. Thus, it can be concluded that, academic achievements can be enhanced by understanding as well as capitalizing the students’ types of intelligences and learning styles.
Keywords: Kinesthetic intelligent, verbal intelligent, visual intelligent, learning style, academic performances.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2761193 3D Finite Element Analysis for Mechanics of Soil-Tool Interaction
Authors: A. Armin, R. Fotouhi, W. Szyszkowski
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This paper is part of a study to develop robots for farming. As such power requirement to operate equipment attach to such robots become an important factor. Soil-tool interaction plays major role in power consumption, thus predicting accurately the forces which act on the blade during the farming is very important for optimal designing of farm equipment. In this paper, a finite element investigation for tillage tools and soil interaction is described by using an inelastic constitutive material law for agriculture application. A 3-dimensional (3D) nonlinear finite element analysis (FEA) is developed to examine behavior of a blade with different rake angles moving in a block of soil, and to estimate the blade force. The soil model considered is an elastic-plastic with non-associated Drucker-Prager material model. Special use of contact elements are employed to consider connection between soil-blade and soil-soil surfaces. The FEA results are compared with experimental ones, which show good agreement in accurately predicting draft forces developed on the blade when it moves through the soil. Also a very good correlation was obtained between FEA results and analytical results from classical soil mechanics theories for straight blades. These comparisons verified the FEA model developed. For analyzing complicated soil-tool interactions and for optimum design of blades, this method will be useful.Keywords: Finite element analysis, soil-blade contact modeling, blade force.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2991192 The Non-Stationary BINARMA(1,1) Process with Poisson Innovations: An Application on Accident Data
Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer
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This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.Keywords: Non-stationary, BINARMA(1, 1) model, Poisson Innovations, CML
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 578191 Potential Use of Local Materials as Synthesizing One Part Geopolymer Cement
Authors: Areej Almalkawi, Sameer Hamadna, Parviz Soroushian, Nalin Darsana
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The work on indigenous binders in this paper focused on the following indigenous raw materials: red clay, red lava and pumice (as primary aluminosilicate precursors), wood ash and gypsum (as supplementary minerals), and sodium sulfate and lime (as alkali activators). The experimental methods used for evaluation of these indigenous raw materials included laser granulometry, x-ray fluorescence (XRF) spectroscopy, and chemical reactivity. Formulations were devised for transforming these raw materials into alkali aluminosilicate-based hydraulic cements. These formulations were processed into hydraulic cements via simple heating and milling actions to render thermal activation, mechanochemical and size reduction effects. The resulting hydraulic cements were subjected to laser granulometry, heat of hydration and reactivity tests. These cements were also used to prepare mortar mixtures, which were evaluated via performance of compressive strength tests. The measured values of strength were correlated with the reactivity, size distribution and microstructural features of raw materials. Some of the indigenous hydraulic cements produced in this reporting period yielded viable levels of compressive strength. The correlation trends established in this work are being evaluated for development of simple and thorough methods of qualifying indigenous raw materials for use in production of indigenous hydraulic cements.
Keywords: One-part geopolymer cement, aluminosilicate precursors, thermal activation, mechanochemical.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 685190 A Study on Human Musculoskeletal Model for Cycle Fitting: Comparison with EMG
Authors: Yoon- Ho Shin, Jin-Seung Choi, Dong-Won Kang, Jeong-Woo Seo, Joo-Hack Lee, Ju-Young Kim, Dae-Hyeok Kim, Seung-Tae Yang, Gye-Rae Tack
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It is difficult to study the effect of various variables on cycle fitting through actual experiment. To overcome such difficulty, the forward dynamics of a musculoskeletal model was applied to cycle fitting in this study. The measured EMG data weres compared with the muscle activities of the musculoskeletal model through forward dynamics. EMG data were measured from five cyclists who do not have musculoskeletal diseases during three minutes pedaling with a constant load (150 W) and cadence (90 RPM). The muscles used for the analysis were the Vastus Lateralis (VL), Tibialis Anterior (TA), Bicep Femoris (BF), and Gastrocnemius Medial (GM). Person’s correlation coefficients of the muscle activity patterns, the peak timing of the maximum muscle activities, and the total muscle activities were calculated and compared. BIKE3D model of AnyBody (Anybodytech, Denmark) was used for the musculoskeletal model simulation. The comparisons of the actual experiments with the simulation results showed significant correlations in the muscle activity patterns (VL: 0.789, TA: 0.503, BF: 0.468, GM: 0.670). The peak timings of the maximum muscle activities were distributed at particular phases. The total muscle activities were compared with the normalized muscle activities, and the comparison showed about 10% difference in the VL (+10%), TA (+9.7%), and BF (+10%), excluding the GM (+29.4%). Thus, it can be concluded that muscle activities of model & experiment showed similar results. The results of this study indicated that it was possible to apply the simulation of further improved musculoskeletal model to cycle fitting.
Keywords: Cycle fitting, EMG, Musculoskeletal modeling, Simulation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3160189 Non-Methane Hydrocarbons Emission during the Photocopying Process
Authors: Kiurski S. Jelena, Aksentijević M. Snežana, Kecić S. Vesna, Oros B. Ivana
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Prosperity of electronic equipment in photocopying environment not only has improved work efficiency, but also has changed indoor air quality. Considering the number of photocopying employed, indoor air quality might be worse than in general office environments. Determining the contribution from any type of equipment to indoor air pollution is a complex matter. Non-methane hydrocarbons are known to have an important role on air quality due to their high reactivity. The presence of hazardous pollutants in indoor air has been detected in one photocopying shop in Novi Sad, Serbia. Air samples were collected and analyzed for five days, during 8-hr working time in three time intervals, whereas three different sampling points were determined. Using multiple linear regression model and software package STATISTICA 10 the concentrations of occupational hazards and microclimates parameters were mutually correlated. Based on the obtained multiple coefficients of determination (0.3751, 0.2389 and 0.1975), a weak positive correlation between the observed variables was determined. Small values of parameter F indicated that there was no statistically significant difference between the concentration levels of nonmethane hydrocarbons and microclimates parameters. The results showed that variable could be presented by the general regression model: y = b0 + b1xi1+ b2xi2. Obtained regression equations allow to measure the quantitative agreement between the variables and thus obtain more accurate knowledge of their mutual relations.Keywords: Indoor air quality, multiple regression analysis, nonmethane hydrocarbons, photocopying process.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1965188 Innovative Activity and Development: Analyzing Firm Data from Eurozone Country-Members
Authors: Ilias A. Makris
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In this work, we attempt to associate firm characteristics with innovative activity. We collect microdata from listed firms of selected Eurozone Country-members, after the beginning of 2007 financial crisis. The following literature, several indicators of growth and performance were selected and tested for their ability to interpret innovative activity. The main scope is to examine the possible differences in performance and growth between innovative and non-innovative firms, during a severe recession. Additionally to that, a special focus will be held on whether macroeconomic performance and national innovation system, determines the extent of innovators' performance. Preliminary findings, through correlation matrices and non-parametric tests, strongly indicate the positive relation between innovative activity and most of the measures used (profitability, size, employment), confirming that even during a recessionary period, innovative firms not only survive but also seem to succeed better economic results in almost all indexes relative to non-innovative. However, even though innovators seem to perform better in all economies examined, the extent of that performance seems to be strongly affected by the supportive mechanisms (financial and structural) that their country provides. Thus, it is clear, that the technologically intensive 'gap' between European South and North, during the economic crisis, became chaotic, due to the harsh austerity measures and reduced budgets in those countries, even in sectors with high potentials in economic activity and employment, impairing the effects of crisis and enhancing the vicious circle of recession.Keywords: Eurozone, innovative activity, development, firm performance, non-parametric tests.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1441187 Bone Mineral Density and Quality, Body Composition of Women in the Postmenopausal Period
Authors: Vladyslav Povoroznyuk, Oksana Ivanyk, Nataliia Dzerovych
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In the diagnostics of osteoporosis, the gold standard is considered to be bone mineral density; however, X-ray densitometry is not an accurate indicator of osteoporotic fracture risk under all circumstances. In this regard, the search for new methods that could determine the indicators not only of the mineral density, but of the bone tissue quality, is a logical step for diagnostic optimization. One of these methods is the evaluation of trabecular bone quality. The aim of this study was to examine the quality and mineral density of spine bone tissue, femoral neck, and body composition of women depending on the duration of the postmenopausal period, to determine the correlation of body fat with indicators of bone mineral density and quality. The study examined 179 women in premenopausal and postmenopausal periods. The patients were divided into the following groups: Women in the premenopausal period and women in the postmenopausal period at various stages (early, middle, late postmenopause). A general examination and study of the above parameters were conducted with General Electric X-ray densitometer. The results show that bone quality and mineral density probably deteriorate with advancing of postmenopausal period. Total fat and lean mass ratio is not likely to change with age. In the middle and late postmenopausal periods, the bone tissue mineral density of the spine and femoral neck increases along with total fat mass.
Keywords: Osteoporosis, bone tissue mineral density, bone quality, fat mass, lean mass, postmenopausal osteoporosis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 926