Search results for: liquidliquid extraction modelling

Authors: S. A. Sadegh Zadeh, C. Kambhampati

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Mathematical and computational modellings are the necessary tools for reviewing, analysing, and predicting processes and events in the wide spectrum range of scientific fields. Therefore, in a field as rapidly developing as neuroscience, the combination of these two modellings can have a significant role in helping to guide the direction the field takes. The paper combined mathematical and computational modelling to prove a weakness in a very precious model in neuroscience. This paper is intended to analyse all-or-none principle in Hodgkin-Huxley mathematical model. By implementation the computational model of Hodgkin-Huxley model and applying the concept of all-or-none principle, an investigation on this mathematical model has been performed. The results clearly showed that the mathematical model of Hodgkin-Huxley does not observe this fundamental law in neurophysiology to generating action potentials. This study shows that further mathematical studies on the Hodgkin-Huxley model are needed in order to create a model without this weakness.

Keywords: All-or-none, computational modelling, mathematical model, transmembrane voltage, action potential.

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Authors: Mario Kubek, Herwig Unger

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Due to the large amount of information in the World Wide Web (WWW, web) and the lengthy and usually linearly ordered result lists of web search engines that do not indicate semantic relationships between their entries, the search for topically similar and related documents can become a tedious task. Especially, the process of formulating queries with proper terms representing specific information needs requires much effort from the user. This problem gets even bigger when the user's knowledge on a subject and its technical terms is not sufficient enough to do so. This article presents the new and interactive search application DocAnalyser that addresses this problem by enabling users to find similar and related web documents based on automatic query formulation and state-ofthe- art search word extraction. Additionally, this tool can be used to track topics across semantically connected web documents.

Keywords: DocAnalyser, interactive web search, search word extraction, query formulation, source topic detection, topic tracking.

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Authors: Mahdi Shaneh, Azizollah Taheri

Abstract:

The goal of this project is to design a system to recognition voice commands. Most of voice recognition systems contain two main modules as follow “feature extraction" and “feature matching". In this project, MFCC algorithm is used to simulate feature extraction module. Using this algorithm, the cepstral coefficients are calculated on mel frequency scale. VQ (vector quantization) method will be used for reduction of amount of data to decrease computation time. In the feature matching stage Euclidean distance is applied as similarity criterion. Because of high accuracy of used algorithms, the accuracy of this voice command system is high. Using these algorithms, by at least 5 times repetition for each command, in a single training session, and then twice in each testing session zero error rate in recognition of commands is achieved.

Keywords: MFCC, Vector quantization, Vocal tract, Voicecommand.

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

Abstract:

Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.

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Authors: V. Balanica, W. I. D. Rae, M. Caramihai, S. Acho, C. P. Herbst

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Mammographic images and data analysis to facilitate modelling or computer aided diagnostic (CAD) software development should best be done using a common database that can handle various mammographic image file formats and relate these to other patient information. This would optimize the use of the data as both primary reporting and enhanced information extraction of research data could be performed from the single dataset. One desired improvement is the integration of DICOM file header information into the database, as an efficient and reliable source of supplementary patient information intrinsically available in the images. The purpose of this paper was to design a suitable database to link and integrate different types of image files and gather common information that can be further used for research purposes. An interface was developed for accessing, adding, updating, modifying and extracting data from the common database, enhancing the future possible application of the data in CAD processing. Technically, future developments envisaged include the creation of an advanced search function to selects image files based on descriptor combinations. Results can be further used for specific CAD processing and other research. Design of a user friendly configuration utility for importing of the required fields from the DICOM files must be done.

Keywords: Database Integration, Mammogram Classification, Tumour Classification, Computer Aided Diagnosis.

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Authors: Faisal A. Aburub

Abstract:

Organizations today need to invest in software in order to run their businesses, and to the organizations’ objectives, the software should be in line with the business process. This research presents an approach for linking process models and system models. Particularly, the new approach aims to synthesize sequence diagram based on role activity diagram (RAD) model. The approach includes four steps namely: Create business process model using RAD, identify computerized activities, identify entities in sequence diagram and identify messages in sequence diagram. The new approach has been validated using the process of student registration in University of Petra as a case study. Further research is required to validate the new approach using different domains.

Keywords: Business process modelling, system models, role activity diagrams, sequence diagrams.

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Authors: Carmen Grigoraş, Victor Grigoraş, Daniela Boişteanu

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The standard investigational method for obstructive sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG), which consists of a simultaneous, usually overnight recording of multiple electro-physiological signals related to sleep and wakefulness. This is an expensive, encumbering and not a readily repeated protocol, and therefore there is need for simpler and easily implemented screening and detection techniques. Identification of apnea/hypopnea events in the screening recordings is the key factor for the diagnosis of OSAS. The analysis of a solely single-lead electrocardiographic (ECG) signal for OSAS diagnosis, which may be done with portable devices, at patient-s home, is the challenge of the last years. A novel artificial neural network (ANN) based approach for feature extraction and automatic identification of respiratory events in ECG signals is presented in this paper. A nonlinear principal component analysis (NLPCA) method was considered for feature extraction and support vector machine for classification/recognition. An alternative representation of the respiratory events by means of Kohonen type neural network is discussed. Our prospective study was based on OSAS patients of the Clinical Hospital of Pneumology from Iaşi, Romania, males and females, as well as on non-OSAS investigated human subjects. Our computed analysis includes a learning phase based on cross signal PSG annotation.

Keywords: Artificial neural networks, feature extraction, obstructive sleep apnea syndrome, pattern recognition, signalprocessing.

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Authors: Bhaskar Thakker, Anoop Lal Vyas

Abstract:

Wrist pulse analysis for identification of health status is found in Ancient Indian as well as Chinese literature. The preprocessing of wrist pulse is necessary to remove outlier pulses and fluctuations prior to the analysis of pulse pressure signal. This paper discusses the identification of irregular pulses present in the pulse series and intricacies associated with the extraction of time domain pulse features. An approach of Dynamic Time Warping (DTW) has been utilized for the identification of outlier pulses in the wrist pulse series. The ambiguity present in the identification of pulse features is resolved with the help of first derivative of Ensemble Average of wrist pulse series. An algorithm for detecting tidal and dicrotic notch in individual wrist pulse segment is proposed.

Keywords: Wrist Pulse Segment, Ensemble Average, Dynamic Time Warping (DTW), Pulse Similarity Vector.

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Authors: Khalida Boutemak, Nasssima Benali, Nadji Moulai-Mostefa

Abstract:

Effect of enzyme on the yield and chemical composition of Artemisia campestris essential oil is reported in the present study. It was demonstrated that enzyme facilitated the extraction of essential oil with increase in oil yield and did not affect any noticeable change in flavour profile of the  volatile oil. Essential oil was tested for antibacterial activity using Escherichia coli; which was extremely sensitive against control with the largest inhibition (29mm), whereas Staphylococcus aureus was the most sensitive against essential oil obtained from enzymatic pre-treatment with the largest inhibition zone (25mm). The antioxidant activity of the essential oil with hemicellulase pre-treatment (EO2) and control sample (EO1) was determined through reducing power. It was significantly lower than the standard drug (vitamin C) in this order: vitamin C˃EO2˃EO1.

Keywords: Artemisia campestris, enzyme pre-treatment, hemicellulase, antibacterial activity, antioxidant activity.

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Authors: A. Rouili

Abstract:

In the present paper the results of a numerical study are presented, numerical models were developed to simulate the behaviour of vertical massive dikes. The proposed models were developed according to the geometry, boundary conditions, loading conditions and initial conditions of a physical model taken as reference. The results obtained were compared to the experimental data. As far as the overall behaviour, the displacements and the failure mechanisms of the dikes is concerned, the numerical results were in good agreement with the experimental results, which clearly indicates a good quality of numerical modelling. The validated numerical models were used in a parametric study were the displacements and failure mechanisms were fully investigated. Out of the results obtained, some conclusions and recommendations related to the design of massive dikes are proposed.

Keywords: Water conservation, dikes, risk assessment and numerical modelling.

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: Remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction.

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Authors: Randa I. M. Elanwar, Mohsen A. Rashwan, Samia A. Mashali

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In this paper we present the first Arabic sentence dataset for on-line handwriting recognition written on tablet pc. The dataset is natural, simple and clear. Texts are sampled from daily newspapers. To collect naturally written handwriting, forms are dictated to writers. The current version of our dataset includes 154 paragraphs written by 48 writers. It contains more than 3800 words and more than 19,400 characters. Handwritten texts are mainly written by researchers from different research centers. In order to use this dataset in a recognition system word extraction is needed. In this paper a new word extraction technique based on the Arabic handwriting cursive nature is also presented. The technique is applied to this dataset and good results are obtained. The results can be considered as a bench mark for future research to be compared with.

Keywords: Arabic, Handwriting recognition, on-line dataset.

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Authors: Jing Lu, Malcolm Keech, Weiru Chen

Abstract:

Web usage mining is an interesting application of data mining which provides insight into customer behaviour on the Internet. An important technique to discover user access and navigation trails is based on sequential patterns mining. One of the key challenges for web access patterns mining is tackling the problem of mining richly structured patterns. This paper proposes a novel model called Web Access Patterns Graph (WAP-Graph) to represent all of the access patterns from web mining graphically. WAP-Graph also motivates the search for new structural relation patterns, i.e. Concurrent Access Patterns (CAP), to identify and predict more complex web page requests. Corresponding CAP mining and modelling methods are proposed and shown to be effective in the search for and representation of concurrency between access patterns on the web. From experiments conducted on large-scale synthetic sequence data as well as real web access data, it is demonstrated that CAP mining provides a powerful method for structural knowledge discovery, which can be visualised through the CAP-Graph model.

Keywords: concurrent access patterns (CAP), CAP mining and modelling, CAP-Graph, web access patterns (WAP), WAP-Graph, Web usage mining.

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Authors: V. Kabeer, N.K.Narayanan

Abstract:

This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.

Keywords: Face recognition, Image analysis, Wavelet feature extraction, Pattern recognition, Classifier algorithms

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Authors: D. Lingam, D. R. Seenivasagam, P. Chand, C. Yee, J. Chief, R. Ananthanarayanan

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In a post-pandemic workspace, identifying the barriers, solutions, and strategies related to telecommuting becomes crucial. Amidst the COVID-19 outbreak in 2020, remote work emerged as a vital business continuity measure. This study investigates telecommuting’s modern work model, exploring benefits and obstacles. Utilizing Interpretive Structural Modelling, it uncovers barriers hindering telecommuting adoption. A validated set of thirteen barriers is examined through departmental surveys, revealing interrelationships. The resulting model highlights interactions and dependencies, forming a foundational framework. By addressing dominant barriers, a domino effect on subservient barriers is demonstrated. This research fosters further exploration, proposing management strategies for successful telecommuting adoption, reshaping the traditional workspace.

Keywords: Barriers, interpretive structural modelling, post-pandemic, telecommuting.

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Authors: P. Cermak

Abstract:

This paper shows possibility of extraction Social, Group and Individual Mind from Multiple Agents Rule Bases. Types those Rule bases are selected as two fuzzy systems, namely Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are describing (modeling) agent behavior. Modifying of agent behavior in the time varying environment will be provided by learning fuzzyneural networks and optimization of their parameters with using genetic algorithms in development system FUZNET. Finally, extraction Social, Group and Individual Mind from Multiple Agents Rule Bases are provided by Cognitive analysis and Matching criterion.

Keywords: Mind, Multi-agent system, Cognitive analysis, Fuzzy system, Neural network, Genetic algorithm, Rule base.

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Authors: K.V. Sridhar, Varun Gunnala, K.S.R Krishna Prasad

Abstract:

In this paper three basic approaches and different methods under each of them for extracting region of interest (ROI) from stationary images are explored. The results obtained for each of the proposed methods are shown, and it is demonstrated where each method outperforms the other. Two main problems in ROI extraction: the channel selection problem and the saliency reversal problem are discussed and how best these two are addressed by various methods is also seen. The basic approaches are 1) Saliency based approach 2) Wavelet based approach 3) Clustering based approach. The saliency approach performs well on images containing objects of high saturation and brightness. The wavelet based approach performs well on natural scene images that contain regions of distinct textures. The mean shift clustering approach partitions the image into regions according to the density distribution of pixel intensities. The experimental results of various methodologies show that each technique performs at different acceptable levels for various types of images.

Keywords: clustering, ROI, saliency, wavelets.

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Authors: Suguru Yoshioka, Seiichi Tani, Seinosuke Toda

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In historical science and social science, the influence of natural disaster upon society is a matter of great interest. In recent years, some archives are made through many hands for natural disasters, however it is inefficiency and waste. So, we suppose a computer system to create a historical natural disaster archive. As the target of this analysis, we consider newspaper articles. The news articles are considered to be typical examples that prescribe the temporal relations of affairs for natural disaster. In order to do this analysis, we identify the occurrences in newspaper articles by some index entries, considering the affairs which are specific to natural disasters, and show the temporal relation between natural disasters. We designed and implemented the automatic system of “extraction of the occurrences of natural disaster" and “temporal relation table for natural disaster."

Keywords: Database, digital library, corpus, historical natural disaster, temporal relation

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Authors: Y. H. Tsai, Y. W. Chen

Abstract:

Facial features are frequently used to represent local properties of a human face image in computer vision applications. In this paper, we present a fast algorithm that can extract the facial features online such that they can give a satisfying representation of a face image. It includes one step for a coarse detection of each facial feature by AdaBoost and another one to increase the accuracy of the found points by Active Shape Models (ASM) in the regions of interest. The resulted facial features are evaluated by matching with artificial face models in the applications of physiognomy. The distance measure between the features and those in the fate models from the database is carried out by means of the Hausdorff distance. In the experiment, the proposed method shows the efficient performance in facial feature extractions and online system of physiognomy.

Keywords: Facial feature extraction, AdaBoost, Active shapemodel, Hausdorff distance

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Authors: D. Triantakonstantis, D. Stathakis

Abstract:

Urban areas have been expanded throughout the globe. Monitoring and modelling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modelling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: Artificial Neural Networks, CORINE, Urban Atlas, Urban Growth Prediction.

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Authors: Catherine K. Thubakgale, Richard K.K. Mbaya, Kaby Kabongo

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The morphology, mineralogical and chemical composition of a low-grade nickel ore from Mpumalanga, South Africa, were studied by scanning electron microscope (SEM), X-ray diffraction (XRD) and X-ray fluorescence (XRF), respectively. The ore was subjected to atmospheric agitation leaching using sulphuric acid to investigate the effects of acid concentration, leaching temperature, leaching time and particle size on extraction of nickel and cobalt. Analyses results indicated the ore to be a saprolitic nickel laterite belonging to the serpentine group of minerals. Sulphuric acid was found to be able to extract nickel from the ore. Increased acid concentration and temperature only produced low amounts of nickel but improved cobalt extraction. As high as 77.44% Ni was achieved when leaching a -106+75μm fraction with 4.0M acid concentration at 25oC. The kinetics of nickel leaching from the saprolitic ore were studied and the activation energy was determined to be 18.16kJ/mol. This indicated that nickel leaching reaction was diffusion controlled.

Keywords: Laterite, sulphuric acid, atmospheric leaching, nickel.

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Authors: I. M. Sur, A. M. Chirila-Babau, T. Gabor, V. Micle

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This paper shows researches in order to extract Cr, Cu and Ni from the polluted soils. Research is based on preliminary studies regarding the usage of Thiobacillus ferrooxidans bacterium (9K medium) for bioleaching of soil polluted with heavy metal (Cu, Cr and Ni). The microorganisms (Thiobacillus ferooxidans) selected directly from polluted soil samples were used in this experimental work. Soil samples used in the experimental research were taken from an area polluted with heavy metals from Romania. The soil samples are subjected to the cleaning process using the 9K medium solution (20 mL and 40 mL, respectively), stirred 200 rpm for 20 hours at a controlled temperature (30 ˚C). During the experiment (0, 2, 4, 8 and 20 h), liquid samples have been extracted and analyzed using the Atomic Absorption Spectrophotometer AA-6800 (AAS) in order to determine the Cr, Cu and Ni concentration. Experiments led to the conclusion that these soils can be depolluted by bioleaching, being a biological treatment method involving the use of microorganisms to favor the extraction of Cr, Cu and Ni from polluted soils.

Keywords: Bioleaching, extraction, microorganisms, polluted soil, Thiobacillus ferooxidans.

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Authors: Amirhossein Karamoozian, Desheng Wu, Behzad Abbasnejad

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Building information modelling (BIM) is a new technology to enhance the efficiency of project management in the construction industry. In addition to the potential benefits of this useful technology, there are various risks and obstacles to applying it in construction projects. In this study, a decision making approach is presented for risk assessment in BIM adoption in construction projects. Various risk factors of exerting BIM during different phases of the project lifecycle are identified with the help of Delphi method, experts’ opinions and related literature. Afterward, Shannon’s entropy and Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) are applied to derive priorities of the identified risk factors. Results indicated that lack of knowledge between professional engineers about workflows in BIM and conflict of opinions between different stakeholders are the risk factors with the highest priority.

Keywords: Risk, BIM, Shannon’s entropy, Fuzzy TOPSIS, construction projects.

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Authors: Mounir Sayadi, Farhat Fnaiech

Abstract:

In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.

Keywords: Texture analysis, statistical features, adaptive filters, Chandrasekhar algorithm.

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Authors: Min Ah Kang, Sangbae Jeong, Minsoo Hahn

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This paper presents the source extraction system which can extract only target signals with constraints on source localization in on-line systems. The proposed system is a kind of methods for enhancing a target signal and suppressing other interference signals. But, the performance of proposed system is superior to any other methods and the extraction of target source is comparatively complete. The method has a beamforming concept and uses an improved time-frequency (TF) mask-based BSS algorithm to separate a target signal from multiple noise sources. The target sources are assumed to be in front and test data was recorded in a reverberant room. The experimental results of the proposed method was evaluated by the PESQ score of real-recording sentences and showed a noticeable speech enhancement.

Keywords: Beam forming, Non-stationary noise reduction, Source separation, TF mask.

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Authors: A. Khafaji, R. Saadane, J. El Abbadi, M. Belkasmi

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The main objectif of this paper is to present a tool that we have developed subject to characterize and modelling indoor radio channel propagation at millimetric wave. The tool is based on the ray tracing technique (RTT). As, in realistic environment we cannot neglect the significant impact of Human Body Shadowing and other objects in motion on indoor 60 GHz propagation channel. Hence, our proposed model allows a simulation of propagation in a dynamic indoor environment. First, we describe a model of human body. Second, RTT with this model is used to simulate the propagation of millimeter waves in the presence of persons in motion. Results of the simulation show that this tool gives results in agreement with those reported in the literature. Specially, the effects of people motion on temporal channel properties.

Keywords: Simulation. 60 GHz band, Ray Tracing Technique, Indoor channel, Propagation, Human Body Model, Level crossing rate,

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Authors: Abdulrahman A. Alghamdi, Paul M. Mummery, Mohammad A. Sheikh

Abstract:

In this paper, 3D image based composite unit cell is constructed from high resolution tomographic images. Through-thickness thermal diffusivity and in-plane Young’s modulus are predicted for the composite unit cell. The accuracy of the image based composite unit cell is tested by comparing its results with the experimental results obtained from laser flash and tensile test. The FE predictions are in close agreement with experimental results. Through-thickness thermal diffusivity and in-plane Young’s modulus of a virgin C/C composite are predicted by replacing the properties of air (porosity) with the properties of carbon matrix. The effect of porosity was found to be more profound on thermal diffusivity than young’s modulus.

Keywords: Porosity, C/C composite, image based FE modelling, CMC.

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Authors: Ž. Nikolić, H. Smoljanović, N. Živaljić

Abstract:

This paper presents numerical model based on finite-discrete element method for analysis of the structural response of dry stone masonry structures under static and dynamic loads. More precisely, each discrete stone block is discretized by finite elements. Material non-linearity including fracture and fragmentation of discrete elements as well as cyclic behavior during dynamic load are considered through contact elements which are implemented within a finite element mesh. The application of the model was conducted on several examples of these structures. The performed analysis shows high accuracy of the numerical results in comparison with the experimental ones and demonstrates the potential of the finite-discrete element method for modelling of the response of dry stone masonry structures.

Keywords: Finite-discrete element method, dry stone masonry structures, static load, dynamic load.

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Authors: Peiman Ariaii, Hamid Tavakolipour, Mohsen Pirdashti, Rabehe Izadi Amoli

Abstract:

Raisin Concentrate (RC) are the most important products obtained in the raisin processing industries. These RC products are now used to make the syrups, drinks and confectionery productions and introduced as natural substitute for sugar in food applications. Iran is a one of the biggest raisin exporter in the world but unfortunately despite a good raw material, no serious effort to extract the RC has been taken in Iran. Therefore, in this paper, we determined and analyzed affected parameters on extracting RC process and then optimizing these parameters for design the extracting RC process in two types of raisin (round and long) produced in Khorasan region. Two levels of solvent (1:1 and 2:1), three levels of extraction temperature (60°C, 70°C and 80°C), and three levels of concentration temperature (50°C, 60°C and 70°C) were the treatments. Finally physicochemical characteristics of the obtained concentrate such as color, viscosity, percentage of reduction sugar, acidity and the microbial tests (mould and yeast) were counted. The analysis was performed on the basis of factorial in the form of completely randomized design (CRD) and Duncan's multiple range test (DMRT) was used for the comparison of the means. Statistical analysis of results showed that optimal conditions for production of concentrate is round raisins when the solvent ratio was 2:1 with extraction temperature of 60°C and then concentration temperature of 50°C. Round raisin is cheaper than the long one, and it is more economical to concentrate production. Furthermore, round raisin has more aromas and the less color degree with increasing the temperature of concentration and extraction. Finally, according to mentioned factors the concentrate of round raisin is recommended.

Keywords: Raisin concentrate, optimization, process parameters, round raisin, Iran.

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Authors: So-young Jung, Bu-chuhl Choe, Gi-young Shin, Jung-hun Kim, Young-zoo Chae

Abstract:

A sensitive and specific method for quantitative determination of aflatoxins(B1, B2, G1,G2), deoxynivalenol, fumonisin(B1,B2), ochratoxin A, zearalenone, T-2 and HT-2 in roasted and ground grains using liquid chromatography combined with tandem mass spectrometry. A double extraction using a phosphate buffer solution followed by methanol was applied to achieve effective co extraction of 11 mycotoxins. A multitoxin immunoaffinity column for all these mycotoxins was used to clean up the extract. The LODs of mycotoxins were 0.1~6.1 μg/kg, LOQs were 0.3~18.4 μg/kg. Forty seven samples collected from Seoul (Korea) for mycotoxin contamination monitoring. The results showed that the occurrence of zearalenone and deoxynivalenol were frequent. Zearalenone was detected in all samples and deoxynivalenol was detected in 80.9 % samples in the range 0.626 ~ 29.264 μg/kg and N.D ~ 48.332 μg/kg respectively. Fumonisins and ochratoxin A were detected in 46.8% samples and 17 % samples respectively, aflatoxins and T-2/HT-2 toxins were not detected all samples.

Keywords: LC-MS/MS, mycotoxins, roasted and ground grains.

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