Search results for: prediction interval
1698 Prediction of Nonlinear Torsional Behavior of High Strength RC Beams
Authors: Woo-Young Jung, Minho Kwon
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Seismic design criteria based on performance of structures have recently been adopted by practicing engineers in response to destructive earthquakes. A simple but efficient structural-analysis tool capable of predicting both the strength and ductility is needed to analyze reinforced concrete (RC) structures under such event. A three-dimensional lattice model is developed in this study to analyze torsions in high-strength RC members. Optimization techniques for determining optimal variables in each lattice model are introduced. Pure torsion tests of RC members are performed to validate the proposed model. Correlation studies between the numerical and experimental results confirm that the proposed model is well capable of representing salient features of the experimental results.Keywords: torsion, non-linear analysis, three-dimensional lattice, high-strength concrete
Procedia PDF Downloads 3511697 Electromagnetic Assessment of Submarine Power Cable Degradation Using Finite Element Method and Sensitivity Analysis
Authors: N. Boutra, N. Ravot, J. Benoit, O. Picon
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Submarine power cables used for offshore wind farms electric energy distribution and transmission are subject to numerous threats. Some of the risks are associated with transport, installation and operating in harsh marine environment. This paper describes the feasibility of an electromagnetic low frequency sensing technique for submarine power cable failure prediction. The impact of a structural damage shape and material variability on the induced electric field is evaluated. The analysis is performed by modeling the cable using the finite element method, we use sensitivity analysis in order to identify the main damage characteristics affecting electric field variation. Lastly, we discuss the results obtained.Keywords: electromagnetism, finite element method, sensitivity analysis, submarine power cables
Procedia PDF Downloads 3591696 Analytical and Statistical Study of the Parameters of Expansive Soil
Authors: A. Medjnoun, R. Bahar
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The disorders caused by the shrinking-swelling phenomenon are prevalent in arid and semi-arid in the presence of swelling clay. This soil has the characteristic of changing state under the effect of water solicitation (wetting and drying). A set of geotechnical parameters is necessary for the characterization of this soil type, such as state parameters, physical and chemical parameters and mechanical parameters. Some of these tests are very long and some are very expensive, hence the use or methods of predictions. The complexity of this phenomenon and the difficulty of its characterization have prompted researchers to use several identification parameters in the prediction of swelling potential. This document is an analytical and statistical study of geotechnical parameters affecting the potential of swelling clays. This work is performing on a database obtained from investigations swelling Algerian soil. The obtained observations have helped us to understand the soil swelling structure and its behavior.Keywords: analysis, estimated model, parameter identification, swelling of clay
Procedia PDF Downloads 4191695 Modern State of the Universal Modeling for Centrifugal Compressors
Authors: Y. Galerkin, K. Soldatova, A. Drozdov
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The 6th version of Universal modeling method for centrifugal compressor stage calculation is described. Identification of the new mathematical model was made. As a result of identification the uniform set of empirical coefficients is received. The efficiency definition error is 0,86 % at a design point. The efficiency definition error at five flow rate points (except a point of the maximum flow rate) is 1,22 %. Several variants of the stage with 3D impellers designed by 6th version program and quasi three-dimensional calculation programs were compared by their gas dynamic performances CFD (NUMECA FINE TURBO). Performance comparison demonstrated general principles of design validity and leads to some design recommendations.Keywords: compressor design, loss model, performance prediction, test data, model stages, flow rate coefficient, work coefficient
Procedia PDF Downloads 4141694 On the Use of Analytical Performance Models to Design a High-Performance Active Queue Management Scheme
Authors: Shahram Jamali, Samira Hamed
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One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.Keywords: active queue management, RED, Markov model, random early detection algorithm
Procedia PDF Downloads 5421693 Using Historical Data for Stock Prediction
Authors: Sofia Stoica
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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.Keywords: finance, machine learning, opening price, stock market
Procedia PDF Downloads 1971692 A DFT-Based QSARs Study of Kovats Retention Indices of Adamantane Derivatives
Authors: Z. Bayat
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A quantitative structure–property relationship (QSPR) study was performed to develop models those relate the structures of 65 Kovats retention index (RI) of adamantane derivatives. Molecular descriptors derived solely from 3D structures of the molecular compounds. The usefulness of the quantum chemical descriptors, calculated at the level of the DFT theories using 6-311+G** basis set for QSAR study of adamantane derivatives was examined. The use of descriptors calculated only from molecular structure eliminates the need to experimental determination of properties for use in the correlation and allows for the estimation of RI for molecules not yet synthesized. The prediction results are in good agreement with the experimental value. A multi-parametric equation containing maximum Four descriptors at B3LYP/6-31+G** method with good statistical qualities (R2train=0.913, Ftrain=97.67, R2test=0.770, Ftest=3.21, Q2LOO=0.895, R2adj=0.904, Q2LGO=0.844) was obtained by Multiple Linear Regression using stepwise method.Keywords: DFT, adamantane, QSAR, Kovat
Procedia PDF Downloads 3681691 Prediction of the Tunnel Fire Flame Length by Hybrid Model of Neural Network and Genetic Algorithms
Authors: Behzad Niknam, Kourosh Shahriar, Hassan Madani
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This paper demonstrates the applicability of Hybrid Neural Networks that combine with back propagation networks (BPN) and Genetic Algorithms (GAs) for predicting the flame length of tunnel fire A hybrid neural network model has been developed to predict the flame length of tunnel fire based parameters such as Fire Heat Release rate, air velocity, tunnel width, height and cross section area. The network has been trained with experimental data obtained from experimental work. The hybrid neural network model learned the relationship for predicting the flame length in just 3000 training epochs. After successful learning, the model predicted the flame length.Keywords: tunnel fire, flame length, ANN, genetic algorithm
Procedia PDF Downloads 6481690 Structural Testing and the Finite Element Modelling of Anchors Loaded Against Partially Confined Surfaces
Authors: Ali Karrech, Alberto Puccini, Ben Galvin, Davide Galli
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This paper summarises the laboratory tests, numerical models and statistical approach developed to investigate the behaviour of concrete blocks loaded in shear through metallic anchors. This research is proposed to bridge a gap in the state of the art and practice related to anchors loaded against partially confined concrete surfaces. Eight concrete blocks (420 mm x 500 mm x 1000 mm) with 150 and/or 250 deep anchors were tested. The stainless-steel anchors of diameter 16 mm were bonded with HIT-RE 500 V4 injection epoxy resin and were subjected to shear loading against partially supported edges. In addition, finite element models were constructed to validate the laboratory tests and explore the influence of key parameters such as anchor depth, anchor distance from the edge, and compressive strength on the stability of the block. Upon their validation experimentally, the numerical results were used to populate, develop and interpret a systematic parametric study based on the Design of Experiment approach through the Box-Behnken design and Response Surface Methodology. An empirical model has been derived based on this approach, which predicts the load capacity with the desirable intervals of confidence.Keywords: finite element modelling, design of experiment, response surface methodology, Box-Behnken design, empirical model, interval of confidence, load capacity
Procedia PDF Downloads 281689 Mechanical Properties and Microstructure of Ultra-High Performance Concrete Containing Fly Ash and Silica Fume
Authors: Jisong Zhang, Yinghua Zhao
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The present study investigated the mechanical properties and microstructure of Ultra-High Performance Concrete (UHPC) containing supplementary cementitious materials (SCMs), such as fly ash (FA) and silica fume (SF), and to verify the synergistic effect in the ternary system. On the basis of 30% fly ash replacement, the incorporation of either 10% SF or 20% SF show a better performance compared to the reference sample. The efficiency factor (k-value) was calculated as a synergistic effect to predict the compressive strength of UHPC with these SCMs. The SEM of micrographs and pore volume from BJH method indicate a high correlation with compressive strength. Further, an artificial neural networks model was constructed for prediction of the compressive strength of UHPC containing these SCMs.Keywords: artificial neural network, fly ash, mechanical properties, ultra-high performance concrete
Procedia PDF Downloads 4171688 UniFi: Universal Filter Model for Image Enhancement
Authors: Aleksei Samarin, Artyom Nazarenko, Valentin Malykh
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Image enhancement is becoming more and more popular, especially on mobile devices. Nowadays, it is a common approach to enhance an image using a convolutional neural network (CNN). Such a network should be of significant size; otherwise, a possibility for the artifacts to occur is overgrowing. The existing large CNNs are computationally expensive, which could be crucial for mobile devices. Another important flaw of such models is they are poorly interpretable. There is another approach to image enhancement, namely, the usage of predefined filters in combination with the prediction of their applicability. We present an approach following this paradigm, which outperforms both existing CNN-based and filter-based approaches in the image enhancement task. It is easily adaptable for mobile devices since it has only 47 thousand parameters. It shows the best SSIM 0.919 on RANDOM250 (MIT Adobe FiveK) among small models and is thrice faster than previous models.Keywords: universal filter, image enhancement, neural networks, computer vision
Procedia PDF Downloads 1031687 Automatic Registration of Rail Profile Based Local Maximum Curvature Entropy
Authors: Hao Wang, Shengchun Wang, Weidong Wang
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On the influence of train vibration and environmental noise on the measurement of track wear, we proposed a method for automatic extraction of circular arc on the inner or outer side of the rail waist and achieved the high-precision registration of rail profile. Firstly, a polynomial fitting method based on truncated residual histogram was proposed to find the optimal fitting curve of the profile and reduce the influence of noise on profile curve fitting. Then, based on the curvature distribution characteristics of the fitting curve, the interval search algorithm based on dynamic window’s maximum curvature entropy was proposed to realize the automatic segmentation of small circular arc. At last, we fit two circle centers as matching reference points based on small circular arcs on both sides and realized the alignment from the measured profile to the standard designed profile. The static experimental results show that the mean and standard deviation of the method are controlled within 0.01mm with small measurement errors and high repeatability. The dynamic test also verified the repeatability of the method in the train-running environment, and the dynamic measurement deviation of rail wear is within 0.2mm with high repeatability.Keywords: curvature entropy, profile registration, rail wear, structured light, train-running
Procedia PDF Downloads 2661686 Data-Driven Performance Evaluation of Surgical Doctors Based on Fuzzy Analytic Hierarchy Processes
Authors: Yuguang Gao, Qiang Yang, Yanpeng Zhang, Mingtao Deng
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To enhance the safety, quality and efficiency of healthcare services provided by surgical doctors, we propose a comprehensive approach to the performance evaluation of individual doctors by incorporating insights from performance data as well as views of different stakeholders in the hospital. Exploratory factor analysis was first performed on collective multidimensional performance data of surgical doctors, where key factors were extracted that encompass assessment of professional experience and service performance. A two-level indicator system was then constructed, for which we developed a weighted interval-valued spherical fuzzy analytic hierarchy process to analyze the relative importance of the indicators while handling subjectivity and disparity in the decision-making of multiple parties involved. Our analytical results reveal that, for the key factors identified as instrumental for evaluating surgical doctors’ performance, the overall importance of clinical workload and complexity of service are valued more than capacity of service and professional experience, while the efficiency of resource consumption ranks comparatively the lowest in importance. We also provide a retrospective case study to illustrate the effectiveness and robustness of our quantitative evaluation model by assigning meaningful performance ratings to individual doctors based on the weights developed through our approach.Keywords: analytic hierarchy processes, factor analysis, fuzzy logic, performance evaluation
Procedia PDF Downloads 611685 ANN Modeling for Cadmium Biosorption from Potable Water Using a Packed-Bed Column Process
Authors: Dariush Jafari, Seyed Ali Jafari
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The recommended limit for cadmium concentration in potable water is less than 0.005 mg/L. A continuous biosorption process using indigenous red seaweed, Gracilaria corticata, was performed to remove cadmium from the potable water. The process was conducted under fixed conditions and the breakthrough curves were achieved for three consecutive sorption-desorption cycles. A modeling based on Artificial Neural Network (ANN) was employed to fit the experimental breakthrough data. In addition, a simplified semi empirical model, Thomas, was employed for this purpose. It was found that ANN well described the experimental data (R2>0.99) while the Thomas prediction were a bit less successful with R2>0.97. The adjusted design parameters using the nonlinear form of Thomas model was in a good agreement with the experimentally obtained ones. The results approve the capability of ANN to predict the cadmium concentration in potable water.Keywords: ANN, biosorption, cadmium, packed-bed, potable water
Procedia PDF Downloads 4351684 The Direct Deconvolution Model for the Large Eddy Simulation of Turbulence
Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang
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Large eddy simulation (LES) has been extensively used in the investigation of turbulence. LES calculates the grid-resolved large-scale motions and leaves small scales modeled by sublfilterscale (SFS) models. Among the existing SFS models, the deconvolution model has been used successfully in the LES of the engineering flows and geophysical flows. Despite the wide application of deconvolution models, the effects of subfilter scale dynamics and filter anisotropy on the accuracy of SFS modeling have not been investigated in depth. The results of LES are highly sensitive to the selection of filters and the anisotropy of the grid, which has been overlooked in previous research. In the current study, two critical aspects of LES are investigated. Firstly, we analyze the influence of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) at varying filter-to-grid ratios (FGR) in isotropic turbulence. An array of invertible filters are employed, encompassing Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The significance of FGR becomes evident, as it acts as a pivotal factor in error control for precise SFS stress prediction. When FGR is set to 1, the DDM models cannot accurately reconstruct the SFS stress due to the insufficient resolution of SFS dynamics. Notably, prediction capabilities are enhanced at an FGR of 2, resulting in accurate SFS stress reconstruction, except for cases involving Helmholtz I and II filters. A remarkable precision close to 100% is achieved at an FGR of 4 for all DDM models. Additionally, the further exploration extends to the filter anisotropy to address its impact on the SFS dynamics and LES accuracy. By employing dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with the anisotropic filter, aspect ratios (AR) ranging from 1 to 16 in LES filters are evaluated. The findings highlight the DDM's proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. High correlation coefficients exceeding 90% are observed in the a priori study for the DDM's reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as lter anisotropy increases. In the a posteriori studies, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, encompassing velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strain-rate tensors, and SFS stress. It is observed that as filter anisotropy intensify, the results of DSM and DMM become worse, while the DDM continues to deliver satisfactory results across all filter-anisotropy scenarios. The findings emphasize the DDM framework's potential as a valuable tool for advancing the development of sophisticated SFS models for LES of turbulence.Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence
Procedia PDF Downloads 761683 Soybean Seed Composition Prediction From Standing Crops Using Planet Scope Satellite Imagery and Machine Learning
Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi
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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.Keywords: agriculture, computer vision, data science, geospatial technology
Procedia PDF Downloads 1391682 Black-Box-Base Generic Perturbation Generation Method under Salient Graphs
Authors: Dingyang Hu, Dan Liu
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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.Keywords: adversarial sample, gradient, probability, black box
Procedia PDF Downloads 1071681 Gas Holdups in a Gas-Liquid Upflow Bubble Column With Internal
Authors: C. Milind Caspar, Valtonia Octavio Massingue, K. Maneesh Reddy, K. V. Ramesh
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Gas holdup data were obtained from measured pressure drop values in a gas-liquid upflow bubble column in the presence of string of hemispheres promoter internal. The parameters that influenced the gas holdup are gas velocity, liquid velocity, promoter rod diameter, pitch and base diameter of hemisphere. Tap water was used as liquid phase and nitrogen as gas phase. About 26 percent in gas holdup was obtained due to the insertion of promoter in in the present study in comparison with empty conduit. Pitch and rod diameter have not shown any influence on gas holdup whereas gas holdup was strongly influenced by gas velocity, liquid velocity and hemisphere base diameter. Correlation equation was obtained for the prediction of gas holdup by least squares regression analysis.Keywords: bubble column, gas-holdup, two-phase flow, turbulent promoter
Procedia PDF Downloads 1081680 The Relationship between EFL Learners' Self-Regulation and Willingness to Communicate
Authors: Mania Nosratinia, Zahra Deris
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The purpose of the present study was to investigate the relationship between EFL learners' self-regulation (SR) and willingness to communicate (WTC). To this end, 520 male and female EFL learners, ranging between 19 and 34 years old (Mage = 26), majoring in English Translation, English Language Teaching and English Literature at Islamic Azad University, Fars Province, were randomly selected. They were given two questionnaires: Self-Regulation Questionnaire devised by Brown, Miller, and Lawendowski (1999) and Willingness to Communicate Scale devised by McCroskey and Baer (1985). Preliminarily, pertinent analyses were performed on the data to check the assumptions of normality, linearity, and homoscedasticity. Since the assumption of normality was violated, Spearman's rank-order correlation was employed to probe the relationships between SR and WTC. The results indicated a significant and positive correlation between the two variables, ρ = .56, n = 520, p < .05, which signified a large effect size supplemented by a very small confidence interval (0.503 – 0.619). The results of the Kruskal-Wallis tests indicated that there is a statistically significant difference in WTC score between the different levels of SR, χ2(2) = 157.843, p = 0.000 with a mean rank SR score of 128.13 for low-SR level, 286.64 for mid-SR level, and 341.12 for high-SR level. Also, a post-hoc comparison through running a Dwass-Steel-Critchlow-Fligner indicated significant differences among the SR level groups on WTC scores. Given the findings of the study, the obtained results may help EFL teachers, teacher trainers, and material developers to possess a broader perspective towards the TEFL practice and to take practical steps towards the attainments of the desired objectives and effective instruction.Keywords: EFL learner, self-regulation, willingness to communicate, relationship
Procedia PDF Downloads 3331679 Predicting the Exposure Level of Airborne Contaminants in Occupational Settings via the Well-Mixed Room Model
Authors: Alireza Fallahfard, Ludwig Vinches, Stephane Halle
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In the workplace, the exposure level of airborne contaminants should be evaluated due to health and safety issues. It can be done by numerical models or experimental measurements, but the numerical approach can be useful when it is challenging to perform experiments. One of the simplest models is the well-mixed room (WMR) model, which has shown its usefulness to predict inhalation exposure in many situations. However, since the WMR is limited to gases and vapors, it cannot be used to predict exposure to aerosols. The main objective is to modify the WMR model to expand its application to exposure scenarios involving aerosols. To reach this objective, the standard WMR model has been modified to consider the deposition of particles by gravitational settling and Brownian and turbulent deposition. Three deposition models were implemented in the model. The time-dependent concentrations of airborne particles predicted by the model were compared to experimental results conducted in a 0.512 m3 chamber. Polystyrene particles of 1, 2, and 3 µm in aerodynamic diameter were generated with a nebulizer under two air changes per hour (ACH). The well-mixed condition and chamber ACH were determined by the tracer gas decay method. The mean friction velocity on the chamber surfaces as one of the input variables for the deposition models was determined by computational fluid dynamics (CFD) simulation. For the experimental procedure, the particles were generated until reaching the steady-state condition (emission period). Then generation stopped, and concentration measurements continued until reaching the background concentration (decay period). The results of the tracer gas decay tests revealed that the ACHs of the chamber were: 1.4 and 3.0, and the well-mixed condition was achieved. The CFD results showed the average mean friction velocity and their standard deviations for the lowest and highest ACH were (8.87 ± 0.36) ×10-2 m/s and (8.88 ± 0.38) ×10-2 m/s, respectively. The numerical results indicated the difference between the predicted deposition rates by the three deposition models was less than 2%. The experimental and numerical aerosol concentrations were compared in the emission period and decay period. In both periods, the prediction accuracy of the modified model improved in comparison with the classic WMR model. However, there is still a difference between the actual value and the predicted value. In the emission period, the modified WMR results closely follow the experimental data. However, the model significantly overestimates the experimental results during the decay period. This finding is mainly due to an underestimation of the deposition rate in the model and uncertainty related to measurement devices and particle size distribution. Comparing the experimental and numerical deposition rates revealed that the actual particle deposition rate is significant, but the deposition mechanisms considered in the model were ten times lower than the experimental value. Thus, particle deposition was significant and will affect the airborne concentration in occupational settings, and it should be considered in the airborne exposure prediction model. The role of other removal mechanisms should be investigated.Keywords: aerosol, CFD, exposure assessment, occupational settings, well-mixed room model, zonal model
Procedia PDF Downloads 1041678 Diagnosis of Diabetes Using Computer Methods: Soft Computing Methods for Diabetes Detection Using Iris
Authors: Piyush Samant, Ravinder Agarwal
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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction
Procedia PDF Downloads 4091677 Effect of Organochlorine Insecticide (Endosulfan) on Albino Rat at the Rate of Blood Uric Acid Level
Authors: Bindu Kumari, Bindu Kumari Singh
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Endosulfan is known to be one of the highly toxic agricultural pesticides commonly used in our societies. With the widespread use of Endosulfan in agriculture, human beings are most likely to be exposed to it, either orally by eating Endosulfan-contaminated foods or by nose and whole body inhalation in the farms during its application. The present study was conducted to observe the changes in the serum uric acid level of the Swiss albino rats due to the administration of Endosulfan. 3.0 mg Endosulfan/kg body weight was daily administered orally to albino rats for 28 days period. Alterations in their K.F.T. parameters were recorded at a regular interval of 7 days within this 28 days period and were compared with those of control rats. All rats were monitored for any observable toxic symptoms throughout the experimental period and they also were weighted weekly to monitor body weight gain. Alteration recorded in K.F.T. parameters within the groups were due to Endosulfan exposure and serum uric acid level was significantly elevated in the 3mg/kg dose group. Pathological changes of rats treated with Endosulfan were observed with typical signs of toxicity. Uric acid is a heterocyclic compound formed as an end product of metabolism of purine nucleotides. It forms ions and salts known as urate and acid urate which are harmful to our health. Uric acid clearance is one of the numerous important functions of the kidney. Defects in this process resulted in Gout, kidney stone or Kidney failure.Keywords: KFT parameters, blood uric acid level, endosulfan, eat
Procedia PDF Downloads 2951676 Fat-Tail Test of Regulatory DNA Sequences
Authors: Jian-Jun Shu
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The statistical properties of CRMs are explored by estimating similar-word set occurrence distribution. It is observed that CRMs tend to have a fat-tail distribution for similar-word set occurrence. Thus, the fat-tail test with two fatness coefficients is proposed to distinguish CRMs from non-CRMs, especially from exons. For the first fatness coefficient, the separation accuracy between CRMs and exons is increased as compared with the existing content-based CRM prediction method – fluffy-tail test. For the second fatness coefficient, the computing time is reduced as compared with fluffy-tail test, making it very suitable for long sequences and large data-base analysis in the post-genome time. Moreover, these indexes may be used to predict the CRMs which have not yet been observed experimentally. This can serve as a valuable filtering process for experiment.Keywords: statistical approach, transcription factor binding sites, cis-regulatory modules, DNA sequences
Procedia PDF Downloads 2941675 A Comparative Study of Force Prediction Models during Static Bending Stage for 3-Roller Cone Frustum Bending
Authors: Mahesh Chudasama, Harit Raval
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Conical sections and shells of metal plates manufactured by 3-roller conical bending process are widely used in the industries. The process is completed by first bending the metal plates statically and then dynamic roller bending sequentially. It is required to have an analytical model to get maximum bending force, for optimum design of the machine, for static bending stage. Analytical models assuming various stress conditions are considered and these analytical models are compared considering various parameters and reported in this paper. It is concluded from the study that for higher bottom roller inclination, the shear stress affects greatly to the static bending force whereas for lower bottom roller inclination it can be neglected.Keywords: roller-bending, static-bending, stress-conditions, analytical-modeling
Procedia PDF Downloads 2541674 Prediction of Index-Mechanical Properties of Pyroclastic Rock Utilizing Electrical Resistivity Method
Authors: İsmail İnce
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The aim of this study is to determine index and mechanical properties of pyroclastic rock in a practical way by means of electrical resistivity method. For this purpose, electrical resistivity, uniaxial compressive strength, point load strength, P-wave velocity, density and porosity values of 10 different pyroclastic rocks were measured in the laboratory. A simple regression analysis was made among the index-mechanical properties of the samples compatible with electrical resistivity values. A strong exponentially relation was found between index-mechanical properties and electrical resistivity values. The electrical resistivity method can be used to assess the engineering properties of the rock from which it is difficult to obtain regular shaped samples as a non-destructive method.Keywords: electrical resistivity, index-mechanical properties, pyroclastic rocks, regression analysis
Procedia PDF Downloads 4791673 Heat Transfer Enhancement by Turbulent Impinging Jet with Jet's Velocity Field Excitations Using OpenFOAM
Authors: Naseem Uddin
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Impinging jets are used in variety of engineering and industrial applications. This paper is based on numerical simulations of heat transfer by turbulent impinging jet with velocity field excitations using different Reynolds Averaged Navier-Stokes Equations models. Also Detached Eddy Simulations are conducted to investigate the differences in the prediction capabilities of these two simulation approaches. In this paper the excited jet is simulated in non-commercial CFD code OpenFOAM with the goal to understand the influence of dynamics of impinging jet on heat transfer. The jet’s frequencies are altered keeping in view the preferred mode of the jet. The Reynolds number based on mean velocity and diameter is 23,000 and jet’s outlet-to-target wall distance is 2. It is found that heat transfer at the target wall can be influenced by judicious selection of amplitude and frequencies.Keywords: excitation, impinging jet, natural frequency, turbulence models
Procedia PDF Downloads 2751672 Incidence of Cancer in Patients with Alzheimer's Disease: A 11-Year Nationwide Population-Based Study
Authors: Jun Hong Lee
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Background: Alzheimer`s disease (AD) I: creases with age and is characterized by the premature progressive loss of neuronal cell. In contrast, cancer cells have inappropriate cell proliferation and resistance to cell death. Objective: We evaluated the association between cancer and AD and also examined the specific types of cancer. Patients and Methods/Material and Methods: This retrospective, nationwide, longitudinal study used National Health Insurance Service – Senior cohort (NHIS-Senior) 2002-2013, which was released by the KNHIS in 2016, comprising 550,000 random subjects who were selected from over than 60. The study included a cohort of 4,408 patients who were first diagnoses as AD between 2003 and 2005. To match each dementia patient, 19,150 subjects were selected from the database by Propensity Score Matching. Results: We enrolled 4,790 patients for analysis in this cohort and the prevalence of AD was higher in female (19.29%) than in male (17.71%). A higher prevalence of AD was observed in the 70-84 year age group and in the higher income status group. A total of 540 cancers occurred within the observation interval. Overall cancer was less frequent in those with AD (12.25%) than in the control (18.46%), with HR 0.704 (95% Confidence Intervals (CIs)=0.0.64-0.775, p-Value < 0.0001). Conclusion: Our data showed a decreased incidence of overall cancers in patients with AD similar to previous studies. Patients with AD had a significantly decreased risk of colon & rectum, lung and stomach cancer. This finding lower than but consistent with Western countries. We need further investigation of genetic evidence linking AD to cancer.Keywords: Alzheimer, cancer, nationwide, longitudinal study
Procedia PDF Downloads 1831671 Aerodynamic Designing of Supersonic Centrifugal Compressor Stages
Authors: Y. Galerkin, A. Rekstin, K. Soldatova
Abstract:
Universal modeling method well proven for industrial compressors was applied for design of the high flow rate supersonic stage. Results were checked by ANSYS CFX and NUMECA Fine Turbo calculations. The impeller appeared to be very effective at transonic flow velocities. Stator elements efficiency is acceptable at design Mach numbers too. Their loss coefficient versus inlet flow angle performances correlates well with Universal modeling prediction. The impeller demonstrated ability of satisfactory operation at design flow rate. Supersonic flow behavior in the impeller inducer at the shroud blade to blade surface Φdes deserves additional study.Keywords: centrifugal compressor stage, supersonic impeller, inlet flow angle, loss coefficient, return channel, shock wave, vane diffuser
Procedia PDF Downloads 4711670 Infant and Child Mortality among the Low Socio-Economic Households in India
Authors: Narendra Kumar
Abstract:
This study uses data from the ‘National Family Health Survey (NFHS-3) 2005-06’ to investigate the predictors of infant and child mortality among low economic households in East and Northeast region. The cross tabulation, life table survival estimates and Cox proportional hazard model techniques have been used to estimate the predictors of infant and child mortality. The life table survival estimates for infant and child mortality shows that infant mortality in female child is lower in comparison to male child but with child mortality, the rates are higher for female in comparison to male child and the Cox proportional hazard model also give highly significant in female in comparison to male child. The infant and child mortality rates among poor households highest in the Central region followed by North and Northeast region and the lowest in South region in comparison to all regions of India. Education of respondent has been found a significant characteristics in both analyzes, further birth interval, respondent occupation, caste/tribe and place of delivery has substantial impact on infant and child mortality among low economic households in East and Northeast region. Finally these findings specified that an increase in parents’ education, improve health care services and improve socioeconomic conditions of low economic households which should in turn raise infant and child survival and should decrease child mortality among low economic households in India.Keywords: infant, child, mortality, socio-economic, India
Procedia PDF Downloads 3091669 Analysis of High Resolution Seismic Reflection Data to Identify Different Regional Lithologies of the Zaria Batholith Located in the Basement Complex of North Central Nigeria
Authors: Collins C. Chiemeke, A. Onugba, P. Sule
Abstract:
High resolution seismic reflection has recently been carried out on Zaria batholith, with the aim of characterizing the granitic Zaria batholiths in terms of its lithology. The geology of the area has revealed that the older granite outcrops in the vicinity of Zaria are exposures of a syntectonics to late-tectonic granite batholiths which intruded a crystalline gneissic basement during the Pan-African Orogeny. During the data acquisition the geophone were placed at interval of 1 m, variable offset of 1 and 10 m was used. The common midpoint (CMP) method with 12 fold coverage was employed for the survey. Analysis of the generated 3D surface of the p wave velocities from different profiles for densities and bulk modulus revealed that the rock material is more consolidated in South East part of the batholith and less consolidated in the North Western part. This was in conformity with earlier identified geology of the area, with the South Eastern part majorly of granitic outcrop, while the North Western part is characterized with the exposure of gneisses and thick overburden cover. The difference in lithology was also confirmed by the difference in seismic sections and Arial satellite photograph. Hence two major lithologies were identified, the granitic and gneisses complex which are characterized by gradational boundaries.Keywords: basement complex, batholith, high resolution, lithologies, seismic reflection
Procedia PDF Downloads 298