Search results for: protein structure prediction
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3889

Search results for: protein structure prediction

3319 MTSSM - A Framework for Multi-Track Segmentation of Symbolic Music

Authors: Brigitte Rafael, Stefan M. Oertl

Abstract:

Music segmentation is a key issue in music information retrieval (MIR) as it provides an insight into the internal structure of a composition. Structural information about a composition can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. The authors of this paper present the MTSSM framework, a twolayer framework for the multi-track segmentation of symbolic music. The strength of this framework lies in the combination of existing methods for local track segmentation and the application of global structure information spanning via multiple tracks. The first layer of the MTSSM uses various string matching techniques to detect the best candidate segmentations for each track of a multi-track composition independently. The second layer combines all single track results and determines the best segmentation for each track in respect to the global structure of the composition.

Keywords: Pattern Recognition, Music Information Retrieval, Machine Learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1609
3318 Comparative Study of Universities’ Web Structure Mining

Authors: Z. Abdullah, A. R. Hamdan

Abstract:

This paper is meant to analyze the ranking of University of Malaysia Terengganu, UMT’s website in the World Wide Web. There are only few researches have been done on comparing the ranking of universities’ websites so this research will be able to determine whether the existing UMT’s website is serving its purpose which is to introduce UMT to the world. The ranking is based on hub and authority values which are accordance to the structure of the website. These values are computed using two websearching algorithms, HITS and SALSA. Three other universities’ websites are used as the benchmarks which are UM, Harvard and Stanford. The result is clearly showing that more work has to be done on the existing UMT’s website where important pages according to the benchmarks, do not exist in UMT’s pages. The ranking of UMT’s website will act as a guideline for the web-developer to develop a more efficient website.

Keywords: Algorithm, ranking, website, web structure mining.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1656
3317 Effect of Electromagnetic Fields on Structure and Pollen Grains Development in Chenopodium album L

Authors: Leila Amjad, Mahsa Shafighi

Abstract:

The role of the pollen grain, with to the reproductive process of higher plants, is to deliver the spermatic cells to the embryo sac for egg fertilization. The aim of this project was study the effect of electromagnetic fields on structure and pollen grains development in Chenopodium album. Anthers of Chenopodium album L. were collected at different stages of development from control (without electromagnetic field) and plants grown at 10m from the field sources. Structure and development of pollen grains were studied and compared. The studying pollen structure by Light and Scanning electron microscopy showed that electromagnetic fields reduction of pollen grains number and male sterility, thus , in some anthers, pollen grains were attached together and deformed compared to control ones. The data presented suggest that prolonged exposures of plants to magnetic field may cause different biological effects at the cellular tissue and organ levels.

Keywords: Electromagnetic fields, pollen, Chenopodium albumL.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2180
3316 A New Method for Estimation of the Source Coherency Structure of Wideband Sources

Authors: Yong-jun Zhao, Heng-li Zhang, Zong-yun Hu

Abstract:

Based on the sources- smoothed rank profile (SRP) and modified minimum description length (MMDL) principle, a method for estimation of the source coherency structure (SCS) and the number of wideband sources is proposed in this paper. Instead of focusing, we first use a spatial smoothing technique to pre-process the array covariance matrix of each frequency for de-correlating the sources and then use smoothed rank profile to determine the SCS and the number of wideband sources. We demonstrate the availability of the method by numerical simulations.

Keywords: Wideband sources, source coherency structure (SCS), smoothed rank profile (SRP).

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1305
3315 Production Structure Monitoring - A Neurologic Based Approach

Authors: G. Reinhart, J. Pohl

Abstract:

Manufacturing companies are facing a broad variety of challenges caused by a dynamic production environment. To succeed in such an environment, it is crucial to minimize the loss of time required to trigger the adaptation process of a company-s production structures. This paper presents an approach for the continuous monitoring of production structures by neurologic principles. It enhances classical monitoring concepts, which are principally focused on reactive strategies, and enables companies to act proactively. Thereby, strategic aspects regarding the harmonization of certain life cycles are integrated into the decision making process for triggering the reconfiguration process of the production structure.

Keywords: Continuous Factory Planning, Production Structure, Production Management.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1437
3314 Hybrid Method Using Wavelets and Predictive Method for Compression of Speech Signal

Authors: Karima Siham Aoubid, Mohamed Boulemden

Abstract:

The development of the signal compression algorithms is having compressive progress. These algorithms are continuously improved by new tools and aim to reduce, an average, the number of bits necessary to the signal representation by means of minimizing the reconstruction error. The following article proposes the compression of Arabic speech signal by a hybrid method combining the wavelet transform and the linear prediction. The adopted approach rests, on one hand, on the original signal decomposition by ways of analysis filters, which is followed by the compression stage, and on the other hand, on the application of the order 5, as well as, the compression signal coefficients. The aim of this approach is the estimation of the predicted error, which will be coded and transmitted. The decoding operation is then used to reconstitute the original signal. Thus, the adequate choice of the bench of filters is useful to the transform in necessary to increase the compression rate and induce an impercevable distortion from an auditive point of view.

Keywords: Compression, linear prediction analysis, multiresolution analysis, speech signal.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1318
3313 The Effect of Saccharomyces cerevisiae Live Yeast Culture on Microbial Nitrogen Supply to Small Intestine in Male Kivircik Yearlings Fed with Different Forage-Concentrate Ratios

Authors: N. Cetinkaya, N. H. Ozdemir

Abstract:

The aim of the study was to investigate the effect of Saccharomyces cerevisiae (SC) live yeast culture on microbial protein supply to small intestine in Kivircik male yearlings when fed with different ratio of forage and concentrate diets. Four Kivircik male yearlings with permanent rumen canula were used in the experiment. The treatments were allocated to a 4x4 Latin square design. Diet I consisted of 70% alfalfa hay and 30% concentrate, Diet II consisted of 30% alfalfa hay and 70% concentrate, Diet I and II were supplemented with a SC. Daily urine was collected and stored at -20°C until analysis. Calorimetric methods were used for the determination of urinary allantoin and creatinine levels. The estimated microbial N supply to small intestine for Diets I, I+SC, II and II+SC were 2.51, 2.64, 2.95 and 3.43 g N/d respectively. Supplementation of Diets I and II with SC significantly affected the allantoin levels in μmol/W0.75 (p<0.05). Mean creatinine values in μmol/W0.75 and allantoin:creatinine ratios were not significantly different among diets. In conclusion, supplementation with SC live yeast culture had a significant effect on urinary allantoin excretion and microbial protein supply to small intestine in Kivircik yearlings fed with high concentrate Diet II (P<0.05). Hence urinary allantoin excretion may be used as a tool for estimating microbial protein supply in Kivircık yearlings. However, further studies are necessary to understand the metabolism of Saccharomyces cerevisiae live yeast culture with different forage:concentrate ratio in Kıvırcık Yearlings.

Keywords: Allantoin, creatinine, Kivircik yearling, microbial nitrogen, Saccharomyces cerevisiae.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1921
3312 Finite Element Analysis of Different Architectures for Bone Scaffold

Authors: Nimisha R. Shirbhate, Sanjay Bokade

Abstract:

Bone Scaffolds are fundamental architecture or a support structure that allows the regeneration of lost or damaged tissues and they are developed as a crucial tool in biomedical engineering. The structure of bone scaffolds plays an important role in treating bone defects. The shape of the bone scaffold performs a vital role, specifically pore size and shape, which help understand the behavior and strength of the scaffold. In this article, first, fundamental aspects of bone scaffold design are established. Second, the behavior of each architecture of the bone scaffold with biomaterials is discussed. Finally, for each structure, the stress analysis was carried out. This study aimed to design a porous and mechanically strong bone regeneration scaffold that can be successfully manufactured. Four porous architectures of the bone scaffold were designed using Rhinoceros solid modelling software. The structure model consisted of repeatable unit cells arranged in layers to fill the chosen scaffold volume. The mechanical behavior of used biocompatible material is studied with the help of ANSYS 19.2 software. It is also playing significant role to predict the strength of defined structures or 3 dimensional models.

Keywords: Bone scaffold, stress analysis, porous structure, static loading.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 503
3311 Evaluation of Baking Properties and Sensory Quality of Wheat-Cowpea Flour

Authors: Mohamed A. Ahmed, Lydia J. Campbell

Abstract:

The fortified of soft wheat flour with cowpea flour in bread making was investigated. The Soft wheat flour (SWF) was substituted by cowpea flour at levels of 5, 15 and 20%. The protein content of composite breads ranged from 6.1 – 9.9%. Significant difference was observed in moisture, protein and crude fibre contents of control (wheat bread) and composite bread at 5% addition of cowpea. Water absorption capacities of composite flours increased with increasing levels of cowpea flour in the blend. The specific loaf volume decreased significantly with increased cowpea content of blends. The overall acceptability of the 5% cowpea flour content of composite bread was not significantly different from the control (Soft Wheat-bread) but there is significantly different with increasing the levels of cowpea flour in the blend more than 5%.

Keywords: Cowpea flour, wheat flour, baking properties, sensory quality.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2182
3310 Finite Element Modeling of Rotating Mixing of Toothpaste

Authors: Inamullah Bhatti, Ahsanullah Baloch, Khadija Qureshi

Abstract:

The objective of this research is to examine the shear thinning behaviour of mixing flow of non-Newtonian fluid like toothpaste in the dissolution container with rotating stirrer. The problem under investigation is related to the chemical industry. Mixing of fluid is performed in a cylindrical container with rotating stirrer, where stirrer is eccentrically placed on the lid of the container. For the simulation purpose the associated motion of the fluid is considered as revolving of the container, with stick stirrer. For numerical prediction, a time-stepping finite element algorithm in a cylindrical polar coordinate system is adopted based on semi-implicit Taylor-Galerkin/pressure-correction scheme. Numerical solutions are obtained for non-Newtonian fluids employing power law model. Variations with power law index have been analysed, with respect to the flow structure and pressure drop.

Keywords: finite element simulation, mixing fluid, rheology, rotating flow, toothpaste

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2244
3309 Refitting Equations for Peak Ground Acceleration in Light of the PF-L Database

Authors: M. Breška, I. Peruš, V. Stankovski

Abstract:

The number of Ground Motion Prediction Equations (GMPEs) used for predicting peak ground acceleration (PGA) and the number of earthquake recordings that have been used for fitting these equations has increased in the past decades. The current PF-L database contains 3550 recordings. Since the GMPEs frequently model the peak ground acceleration the goal of the present study was to refit a selection of 44 of the existing equation models for PGA in light of the latest data. The algorithm Levenberg-Marquardt was used for fitting the coefficients of the equations and the results are evaluated both quantitatively by presenting the root mean squared error (RMSE) and qualitatively by drawing graphs of the five best fitted equations. The RMSE was found to be as low as 0.08 for the best equation models. The newly estimated coefficients vary from the values published in the original works.

Keywords: Ground Motion Prediction Equations, Levenberg-Marquardt algorithm, refitting PF-L database.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1482
3308 The Design of a Vehicle Traffic Flow Prediction Model for a Gauteng Freeway Based on an Ensemble of Multi-Layer Perceptron

Authors: Tebogo Emma Makaba, Barnabas Ndlovu Gatsheni

Abstract:

The cities of Johannesburg and Pretoria both located in the Gauteng province are separated by a distance of 58 km. The traffic queues on the Ben Schoeman freeway which connects these two cities can stretch for almost 1.5 km. Vehicle traffic congestion impacts negatively on the business and the commuter’s quality of life. The goal of this paper is to identify variables that influence the flow of traffic and to design a vehicle traffic prediction model, which will predict the traffic flow pattern in advance. The model will unable motorist to be able to make appropriate travel decisions ahead of time. The data used was collected by Mikro’s Traffic Monitoring (MTM). Multi-Layer perceptron (MLP) was used individually to construct the model and the MLP was also combined with Bagging ensemble method to training the data. The cross—validation method was used for evaluating the models. The results obtained from the techniques were compared using predictive and prediction costs. The cost was computed using combination of the loss matrix and the confusion matrix. The predicted models designed shows that the status of the traffic flow on the freeway can be predicted using the following parameters travel time, average speed, traffic volume and day of month. The implications of this work is that commuters will be able to spend less time travelling on the route and spend time with their families. The logistics industry will save more than twice what they are currently spending.

Keywords: Bagging ensemble methods, confusion matrix, multi-layer perceptron, vehicle traffic flow.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1765
3307 miR-200c as a Biomarker for 5-FU Chemosensitivity in Colorectal Cancer

Authors: Rezvan Najafi, Korosh Heydari, Massoud Saidijam

Abstract:

5-FU is a chemotherapeutic agent that has been used in colorectal cancer (CRC) treatment. However, it is usually associated with the acquired resistance, which decreases the therapeutic effects of 5-FU. miR-200c is involved in chemotherapeutic drug resistance, but its mechanism is not fully understood. In this study, the effect of inhibition of miR-200c in sensitivity of HCT-116 CRC cells to 5-FU was evaluated. HCT-116 cells were transfected with LNA-anti- miR-200c for 48 h. mRNA expression of miR-200c was evaluated using quantitative real- time PCR. The protein expression of phosphatase and tensin homolog (PTEN) and E-cadherin were analyzed by western blotting. Annexin V and propidium iodide staining assay were applied for apoptosis detection. The caspase-3 activation was evaluated by an enzymatic assay. The results showed LNA-anti-miR-200c inhibited the expression of PTEN and E-cadherin protein, apoptosis and activation of caspase 3 compared with control cells. In conclusion, these results suggest that miR-200c as a prognostic marker can overcome to 5-FU chemoresistance in CRC.

Keywords: Colorectal cancer, miR-200c, 5-FU resistance, E-cadherin, PTEN.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 900
3306 Use of Radial Basis Function Neural Network for Bearing Pressure Prediction of Strip Footing on Reinforced Granular Bed Overlying Weak Soil

Authors: Srinath Shetty K., Shivashankar R., Rashmi P. Shetty

Abstract:

Earth reinforcing techniques have become useful and economical to solve problems related to difficult grounds and provide satisfactory foundation performance. In this context, this paper uses radial basis function neural network (RBFNN) for predicting the bearing pressure of strip footing on reinforced granular bed overlying weak soil. The inputs for the neural network models included plate width, thickness of granular bed and number of layers of reinforcements, settlement ratio, water content, dry density, cohesion and angle of friction. The results indicated that RBFNN model exhibited more than 84 % prediction accuracy, thereby demonstrating its application in a geotechnical problem.

Keywords: Bearing pressure, granular bed, radial basis function neural network, strip footing.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1926
3305 Modelling Indoor Air Carbon Dioxide (CO2)Concentration using Neural Network

Authors: J-P. Skön, M. Johansson, M. Raatikainen, K. Leiviskä, M. Kolehmainen

Abstract:

The use of neural networks is popular in various building applications such as prediction of heating load, ventilation rate and indoor temperature. Significant is, that only few papers deal with indoor carbon dioxide (CO2) prediction which is a very good indicator of indoor air quality (IAQ). In this study, a data-driven modelling method based on multilayer perceptron network for indoor air carbon dioxide in an apartment building is developed. Temperature and humidity measurements are used as input variables to the network. Motivation for this study derives from the following issues. First, measuring carbon dioxide is expensive and sensors power consumptions is high and secondly, this leads to short operating times of battery-powered sensors. The results show that predicting CO2 concentration based on relative humidity and temperature measurements, is difficult. Therefore, more additional information is needed.

Keywords: Indoor air quality, Modelling, Neural networks

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1878
3304 A Microwave Bandstop Filter Using Defected Microstrip Structure

Authors: H. Elftouh, N. T. Amar, M. Aghoutane, M. Boussouis

Abstract:

In this paper, two bandstop filters resonating at 5.25 GHz and 7.3 GHz using Defected Microstrip Structure (DMS) are discussed. These slots are incorporated in the feed lines of filters to perform a serious LC resonance property in certain frequency and suppress the spurious signals. Therefore, this method keeps the filter size unchanged and makes a resonance frequency that is due to the abrupt change of the current path of the filter. If the application requires elimination of this band of frequencies, additional filter elements are required, which can only be accomplished by adding this DMS element resonant at desired frequency band rejection. The filters are optimized and simulated with Computer Simulation Technology (CST) tool.

Keywords: Defected microstrip structure, microstrip filters, passive filter.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1361
3303 Characteristics and Outcomes of COVID-19 Related Stroke: A Cohort Study

Authors: Kasra Afsahi, Maryam Soheilifar

Abstract:

Cerebrovascular accident (CVA) is a neurological side effect of COVID-19 disease wit high rate in pandemics. Effect of COVID-19 disease on disorder is unclear. In this cohort, patients with COVID-19 disease were assessed. 60 CVA cases were assessed in a referral hospital in 2020. The major factor was mortality and the cases were those with and without death. The groups were compared for all features about mortality in the patients with COVID-19 and CVA. Totally 23 out of 60 cases (38.3%) were expired. In univariate analysis there was significant association for death by ischemic heart disease (P = 0.015), high-severity stroke (P = 0.012), high C-reactive protein (CRP) (P = 0.001), high ESR (P = 0.009), pleural effusion (P = 0.005), pericardial effusion (P = 0.027), cardiomegaly (P = 0.005), ground glass opacity (P = 0.001), and consolidation (P = 0.001). Among these factors, there was significant association only for CRP (P = 0.001) and consolidation (P = 0.003) in multivariate analysis. Mortality in the cases with COVID-19-related CVA is one-third and it has relationship to elevated CRP and finding the consolidation in the computerized tomography scan of the lungs.

Keywords: COVID-19, stroke, prognosis, C-reactive protein, CRP.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 319
3302 The Use of Voltage Stability Indices and Proposed Instability Prediction to Coordinate with Protection Systems

Authors: R. Leelaruji, V. Knazkins

Abstract:

This paper proposes a methodology for mitigating the occurrence of cascading failure in stressed power systems. The methodology is essentially based on predicting voltage instability in the power system using a voltage stability index and then devising a corrective action in order to increase the voltage stability margin. The paper starts with a brief description of the cascading failure mechanism which is probable root cause of severe blackouts. Then, the voltage instability indices are introduced in order to evaluate stability limit. The aim of the analysis is to assure that the coordination of protection, by adopting load shedding scheme, capable of enhancing performance of the system after the major location of instability is determined. Finally, the proposed method to generate instability prediction is introduced.

Keywords: Blackouts, cascading failure, voltage stability indices, singular value decomposition, load shedding.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1534
3301 An Exact MCNP Modeling of Pebble Bed Reactors

Authors: Amin Abedi, Naser Vosoughi, Mohammad Bagher Ghofrani

Abstract:

Double heterogeneity of randomly located pebbles in the core and Coated Fuel Particles (CFPs) in the pebbles are specific features in pebble bed reactors and usually, because of difficulty to model with MCNP code capabilities, are neglected. In this study, characteristics of HTR-10, Tsinghua University research reactor, are used and not only double heterogeneous but also truncated CFPs and Pebbles are considered.Firstly, 8335 CFPs are distributed randomly in a pebble and then the core of reactor is filled with those pebbles and graphite pebbles as moderator such that 57:43 ratio of fuel and moderator pebbles is established.Finally, four different core configurations are modeled. They are Simple Cubic (SC) structure with truncated pebbles,SC structure without truncated pebble, and Simple Hexagonal(SH) structure without truncated pebbles and SH structure with truncated pebbles. Results like effective multiplication factor (Keff), critical height,etc. are compared with available data.

Keywords: Double Heterogeneity, HTR-10, MCNP, Pebble Bed Reactor, Stochastic Geometry.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2837
3300 Simulation of Particle Damping under Centrifugal Loads

Authors: Riaz A. Bhatti, Wang Yanrong

Abstract:

Particle damping is a technique to reduce the structural vibrations by means of placing small metallic particles inside a cavity that is attached to the structure at location of high vibration amplitudes. In this paper, we have presented an analytical model to simulate the particle damping of two dimensional transient vibrations in structure operating under high centrifugal loads. The simulation results show that this technique remains effective as long as the ratio of the dynamic acceleration of the structure to the applied centrifugal load is more than 0.1. Particle damping increases with the increase of particle to structure mass ratio. However, unlike to the case of particle damping in the absence of centrifugal loads where the damping efficiency strongly depends upon the size of the cavity, here this dependence becomes very weak. Despite the simplicity of the model, the simulation results are considerably in good agreement with the very scarce experimental data available in the literature for particle damping under centrifugal loads.

Keywords: Impact damping, particle damping, vibration control, vibration suppression.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1786
3299 Disturbance Observer for Lateral Trajectory Tracking Control for Autonomous and Cooperative Driving

Authors: Christian Rathgeber, Franz Winkler, Dirk Odenthal, Steffen Muller

Abstract:

In this contribution a structure for high level lateral vehicle tracking control based on the disturbance observer is presented. The structure is characterized by stationary compensating side forces disturbances and guaranteeing a cooperative behavior at the same time. Driver inputs are not compensated by the disturbance observer. Moreover the structure is especially useful as it robustly stabilizes the vehicle. Therefore the parameters are selected using the Parameter Space Approach. The implemented algorithms are tested in real world scenarios.

Keywords: Disturbance observer, trajectory tracking, robust control, autonomous driving, cooperative driving

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3011
3298 Proteolysis in Serbian Traditional Dry Fermented Sausage Petrovská klobása as Influenced by Different Ripening Processes

Authors: P. M. Ikonić, T. A. Tasić, L. S. Petrović, S. B. Škaljac, M. R. Jokanović, V. M. Tomović, B. V. Šojić, N. R. Džinić, A. M. Torbica, B. B. Ikonić

Abstract:

The aim of the study was to determine how different ripening processes (traditional vs. industrial) influenced the proteolysis in traditional Serbian dry-fermented sausage Petrovská klobása. The obtained results indicated more intensive pH decline (0.7 units after 9 days) in industrially ripened products (I), what had a positive impact on drying process and proteolytic changes in these samples. Thus, moisture content in I sausages was lower at each sampling time, amounting 24.7% at the end of production period (90 days). Likewise, the process of proteolysis was more pronounced in I samples, resulting in higher contents of non-protein nitrogen (NPN) and free amino acids nitrogen (FAAN), as well as in faster and more intensive degradation of myosin (≈220 kDa), actin (≈45 kDa) and other polypeptides during processing. Consequently, the appearance and accumulation of several protein fragments were registered.

Keywords: Dry-fermented sausage, Petrovská klobása, proteolysis, ripening process.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4345
3297 Mining of Interesting Prediction Rules with Uniform Two-Level Genetic Algorithm

Authors: Bilal Alatas, Ahmet Arslan

Abstract:

The main goal of data mining is to extract accurate, comprehensible and interesting knowledge from databases that may be considered as large search spaces. In this paper, a new, efficient type of Genetic Algorithm (GA) called uniform two-level GA is proposed as a search strategy to discover truly interesting, high-level prediction rules, a difficult problem and relatively little researched, rather than discovering classification knowledge as usual in the literatures. The proposed method uses the advantage of uniform population method and addresses the task of generalized rule induction that can be regarded as a generalization of the task of classification. Although the task of generalized rule induction requires a lot of computations, which is usually not satisfied with the normal algorithms, it was demonstrated that this method increased the performance of GAs and rapidly found interesting rules.

Keywords: Classification rule mining, data mining, genetic algorithms.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1582
3296 A Statistical Prediction of Likely Distress in Nigeria Banking Sector Using a Neural Network Approach

Authors: D. A. Farinde

Abstract:

One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This study evaluates the susceptibility of Nigerian banks to failure with a view to identifying ratios and financial data that are sensitive to solvency of the bank. Further, a predictive model is generated to guide all stakeholders in the industry. Thirty quoted banks that had published Annual Reports for the year preceding the consolidation i.e. year 2004 were selected. They were examined for distress using the Multilayer Perceptron Neural Network Analysis. The model was used to analyze further reforms by the Central Bank of Nigeria using published Annual Reports of twenty quoted banks for the year 2008 and 2011. The model can thus be used for future prediction of failure in the Nigerian banking system.

Keywords: Bank, Bankruptcy, Financial Ratios, Neural Network, Multilayer Perceptron, Predictive Model

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2683
3295 Forecasting Direct Normal Irradiation at Djibouti Using Artificial Neural Network

Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

Abstract:

In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: Artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1063
3294 A New Technique for Solar Activity Forecasting Using Recurrent Elman Networks

Authors: Salvatore Marra, Francesco C. Morabito

Abstract:

In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.

Keywords: Elman neural networks, sunspot, solar activity, time series prediction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1841
3293 A Comparison between Hybrid and Experimental Extended Polars for the Numerical Prediction of Vertical-Axis Wind Turbine Performance using Blade Element-Momentum Algorithm

Authors: Gabriele Bedon, Marco Raciti Castelli, Ernesto Benini

Abstract:

A dynamic stall-corrected Blade Element-Momentum algorithm based on a hybrid polar is validated through the comparison with Sandia experimental measurements on a 5-m diameter wind turbine of Troposkien shape. Different dynamic stall models are evaluated. The numerical predictions obtained using the extended aerodynamic coefficients provided by both Sheldal and Klimas and Raciti Castelli et al. are compared to experimental data, determining the potential of the hybrid database for the numerical prediction of vertical-axis wind turbine performances.

Keywords: Darrieus wind turbine, Blade Element-Momentum Theory, extended airfoil database, hybrid database, Sandia 5-m wind turbine.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2540
3292 System Reliability by Prediction of Generator Output and Losses in a Competitive Energy Market

Authors: Perumal Nallagownden, Ravindra N. Mukerjee, Syafrudin Masri

Abstract:

In a competitive energy market, system reliability should be maintained at all times. Power system operation being of online in nature, the energy balance requirements must be satisfied to ensure reliable operation the system. To achieve this, information regarding the expected status of the system, the scheduled transactions and the relevant inputs necessary to make either a transaction contract or a transmission contract operational, have to be made available in real time. The real time procedure proposed, facilitates this. This paper proposes a quadratic curve learning procedure, which enables a generator-s contribution to the retailer demand, power loss of transaction in a line at the retail end and its associated losses for an oncoming operating scenario to be predicted. Matlab program was used to test in on a 24-bus IEE Reliability Test System, and the results are found to be acceptable.

Keywords: Deregulation, learning coefficients, reliability, prediction, competitive energy market.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1465
3291 Intelligent Heart Disease Prediction System Using CANFIS and Genetic Algorithm

Authors: Latha Parthiban, R. Subramanian

Abstract:

Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.

Keywords: CANFIS, genetic algorithms, heart disease, membership function.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3961
3290 Cloning of a β-Glucosidase Gene (BGL1) from Traditional Starter Yeast Saccharomycopsis fibuligera BMQ 908 and Expression in Pichia pastoris

Authors: Le Thuy Mai, Vu Nguyen Thanh

Abstract:

β-Glucosidase is an important enzyme for production of ethanol from lignocellulose. With hydrolytic activity on cellooligosaccharides, especially cellobiose, β-glucosidase removes product inhibitory effect on cellulases and forms fermentable sugars. In this study, β-glucosidase encoding gene (BGL1) from traditional starter yeast Saccharomycosis fibuligera BMQ908 was cloned and expressed in Pichia pastoris. BGL1 of S. fibuligera BMQ 908 shared 98% nucleotide homology with the closest GenBank sequence (M22475) but identity in amino-acid sequences of catalytic domains. Recombinant plasmid pPICZαA/BGL1 containing the sequence encoding BGL1 mature protein and α-factor secretion signal was constructed and transformed into methylotrophic yeast P. pastoris by electroporation. The recombinant strain produced single extracellular protein with molecular weight of 120 kDa and cellobiase activity of 60 IU/ml. The optimum pH of the recombinant β-glucosidase was 5.0 and the optimum temperature was 50°C.

Keywords: β-Glucosidase, Pichia pastoris, Saccharomycopsisfibuligera, recombinant enzyme.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4957