Search results for: Automatic cell counting

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: Akan C. Offong, E. J. Anthony, Vasilije Manovic

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A modified steady-state numerical model is developed for the electrochemical reduction of CO₂ to formic acid. The numerical model achieves a CD (current density) (~60 mA/cm²), FE-faradaic efficiency (~98%) and conversion (~80%) for CO₂ electro-reduction to formic acid in a microfluidic cell. The model integrates charge and species transport, mass conservation, and momentum with electrochemistry. Specifically, the influences of Bi-Sn based nanoparticle catalyst (on the cathode surface) at different mole fractions and 1-ethyl-3-methyl imidazolium tetra-fluoroborate ([EMIM][BF₄]) electrolyte, on CD, FE and CO₂ conversion to formic acid is studied. The reaction is carried out at a constant concentration of electrolyte (85% v/v., [EMIM][BF₄]). Based on the mass transfer characteristics analysis (concentration contours), mole ratio 0.5:0.5 Bi-Sn catalyst displays the highest CO₂ mole consumption in the cathode gas channel. After validating with experimental data (polarisation curves) from literature, extensive simulations reveal performance measure: CD, FE and CO₂ conversion. Increasing the negative cathode potential increases the current densities for both formic acid and H₂ formations. However, H₂ formations are minimal as a result of insufficient hydrogen ions in the ionic liquid electrolyte. Moreover, the limited hydrogen ions have a negative effect on formic acid CD. As CO₂ flow rate increases, CD, FE and CO₂ conversion increases.

Keywords: Carbon dioxide, electro-chemical reduction, microfluidics, ionic liquids, modelling.

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Authors: Jun-Yong Lee, Hyoung-Gook Kim

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In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: Automatic equalization, genre classification, music segment detection, spatial audio processing.

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Authors: Kesari Verma, Aniruddha S. Thoke, Pritam Singh

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Automatic face detection is a complex problem in image processing. Many methods exist to solve this problem such as template matching, Fisher Linear Discriminate, Neural Networks, SVM, and MRC. Success has been achieved with each method to varying degrees and complexities. In proposed algorithm we used upright, frontal faces for single gray scale images with decent resolution and under good lighting condition. In the field of face recognition technique the single face is matched with single face from the training dataset. The author proposed a neural network based face detection algorithm from the photographs as well as if any test data appears it check from the online scanned training dataset. Experimental result shows that the algorithm detected up to 95% accuracy for any image.

Keywords: Face Detection, Face Recognition, NN Approach, PCA Algorithm.

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Authors: S. Chikhi, M. Batouche, H. Shout

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In this paper, we combine a probabilistic neural method with radial-bias functions in order to construct the lithofacies of the wells DF01, DF02 and DF03 situated in the Triassic province of Algeria (Sahara). Lithofacies is a crucial problem in reservoir characterization. Our objective is to facilitate the experts' work in geological domain and to allow them to obtain quickly the structure and the nature of lands around the drilling. This study intends to design a tool that helps automatic deduction from numerical data. We used a probabilistic formalism to enhance the classification process initiated by a Self-Organized Map procedure. Our system gives lithofacies, from well-log data, of the concerned reservoir wells in an aspect easy to read by a geology expert who identifies the potential for oil production at a given source and so forms the basis for estimating the financial returns and economic benefits.

Keywords: Classification, Lithofacies, Probabilistic formalism, Reservoir characterization, Well-log data.

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Authors: Parvinder S. Sandhu, Harpreet Kaur, Amanpreet Singh

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Automatic reusability appraisal is helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this research work, structural attributes of software components are explored using software metrics and quality of the software is inferred by different Neural Network based approaches, taking the metric values as input. The calculated reusability value enables to identify a good quality code automatically. It is found that the reusability value determined is close to the manual analysis used to be performed by the programmers or repository managers. So, the developed system can be used to enhance the productivity and quality of software development.

Keywords: Neural Network, Software Reusability, Software Metric, Accuracy, MAE, RMSE.

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Authors: R. O. Rominu, C. Sinescu, D.M. Pop, M. Hughes, A. Bradu, M. Rominu, A. Gh. Podoleanu

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Bonding has become a routine procedure in several dental specialties – from prosthodontics to conservative dentistry and even orthodontics. In many of these fields it is important to be able to investigate the bonded interfaces to assess their quality. All currently employed investigative methods are invasive, meaning that samples are destroyed in the testing procedure and cannot be used again. We have investigated the interface between human enamel and bonded ceramic brackets non-invasively, introducing a combination of new investigative methods – optical coherence tomography (OCT), fluorescence OCT and confocal microscopy (CM). Brackets were conventionally bonded on conditioned buccal surfaces of teeth. The bonding was assessed using these methods. Three dimensional reconstructions of the detected material defects were developed using manual and semi-automatic segmentation. The results clearly prove that OCT, fluorescence OCT and CM are useful in orthodontic bonding investigations.

Keywords: Optical coherence tomography, Confocal Microscopy, Orthodontic Bonding.

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Authors: Pavel Y. Tabakov, Kevin Duffy

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The present study presents a new approach to automatic data clustering and classification problems in large and complex databases and, at the same time, derives specific types of explicit rules describing each cluster. The method works well in both sparse and dense multidimensional data spaces. The members of the data space can be of the same nature or represent different classes. A number of N-dimensional ellipsoids are used for enclosing the data clouds. Due to the geometry of an ellipsoid and its free rotation in space the detection of clusters becomes very efficient. The method is based on genetic algorithms that are used for the optimization of location, orientation and geometric characteristics of the hyper-ellipsoids. The proposed approach can serve as a basis for the development of general knowledge systems for discovering hidden knowledge and unexpected patterns and rules in various large databases.

Keywords: Classification, clustering, data minig, genetic algorithms.

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Authors: Chareerut Phruksaniyom, Khwanthana Grataitong, Permphan Dharmasaroja, Surapol Issaragrisil

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Bone marrow-derived stem cells have been widely studied as an alternative source of stem cells. Mesenchymal stem cells (MSCs) were mostly investigated and studies showed MSCs can promote neurogenesis. Little is known about the non-mesenchymal mononuclear cell fraction, which contains both hematopoietic and nonhematopoietic cells, including monocytes and endothelial progenitor cells. This study focused on unfractionated bone marrow mononuclear cells (BMMCs), which remained 72 h after MSCs were adhered to the culture plates. We showed that BMMC-conditioned medium promoted morphological changes of human SH-SY5Y neuroblastoma cells from an epithelial-like phenotype towards a neuron-like phenotype as indicated by an increase in neurite outgrowth, like those observed in retinoic acid (RA)-treated cells. The result could be explained by the effects of trophic factors released from BMMCs, as shown in the RT-PCR results that BMMCs expressed nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and ciliary neurotrophic factor (CNTF). Similar results on the cell proliferation rate were also observed between RA-treated cells and cells cultured in BMMC-conditioned medium, suggesting that cells creased proliferating and differentiated into a neuronal phenotype. Using real-time RT-PCR, a significantly increased expression of tyrosine hydroxylase (TH) mRNA in SHSY5Y cells indicated that BMMC-conditioned medium induced catecholaminergic identities in differentiated SH-SY5Y cells.

Keywords: bone marrow, neuronal differentiation, neurite outgrowth, trophic factor, tyrosine hydroxylase

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

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This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: Segmentation, road signs, characters, classification.

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Authors: Sven Borén, Henrik Ny

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Ambitions within the EU for moving towards sustainable transport include major emission reductions for fossil fuel road vehicles, especially for buses, trucks, and cars. The electric driveline seems to be an attractive solution for such development. This study first applied the Framework for Strategic Sustainable Development to compare sustainability effects of today’s fossil fuel vehicles with electric vehicles that have batteries or hydrogen fuel cells. The study then addressed a scenario were electric vehicles might be in majority in Europe by 2050. The methodology called Strategic Lifecycle Assessment was first used, were each life cycle phase was assessed for violations against sustainability principles. This indicates where further analysis could be done in order to quantify the magnitude of each violation, and later to create alternative strategies and actions that lead towards sustainability. A Life Cycle Assessment of combustion engine cars, plug-in hybrid cars, battery electric cars and hydrogen fuel cell cars was then conducted to compare and quantify environmental impacts. The authors found major violations of sustainability principles like use of fossil fuels, which contribute to the increase of emission related impacts such as climate change, acidification, eutrophication, ozone depletion, and particulate matters. Other violations were found, such as use of scarce materials for batteries and fuel cells, and also for most life cycle phases for all vehicles when using fossil fuel vehicles for mining, production and transport. Still, the studied current battery and hydrogen fuel cell cars have less severe violations than fossil fuel cars. The life cycle assessment revealed that fossil fuel cars have overall considerably higher environmental impacts compared to electric cars as long as the latter are powered by renewable electricity. By 2050, there will likely be even more sustainable alternatives than the studied electric vehicles when the EU electricity mix mainly should stem from renewable sources, batteries should be recycled, fuel cells should be a mature technology for use in vehicles (containing no scarce materials), and electric drivelines should have replaced combustion engines in other sectors. An uncertainty for fuel cells in 2050 is whether the production of hydrogen will have had time to switch to renewable resources. If so, that would contribute even more to a sustainable development. Except for being adopted in the GreenCharge roadmap, the authors suggest that the results can contribute to planning in the upcoming decades for a sustainable increase of EVs in Europe, and potentially serve as an inspiration for other smaller or larger regions. Further studies could map the environmental effects in LCA further, and include other road vehicles to get a more precise perception of how much they could affect sustainable development.

Keywords: Strategic, electric vehicles, fuel cell, LCA, sustainability.

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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: Feng-Sheng Wang, Wu-Hsiung Wu, Kai-Cheng Hsu

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The optimization of biological systems, which is a branch of metabolic engineering, has generated a lot of industrial and academic interest for a long time. In the last decade, metabolic engineering approaches based on mathematical optimizations have been used extensively for the analysis and manipulation of metabolic networks. In practical optimization of metabolic reaction networks, designers have to manage the nature of uncertainty resulting from qualitative characters of metabolic reactions, e.g., the possibility of enzyme effects. A deterministic approach does not give an adequate representation for metabolic reaction networks with uncertain characters. Fuzzy optimization formulations can be applied to cope with this problem. A fuzzy multi-objective optimization problem can be introduced for finding the optimal engineering interventions on metabolic network systems considering the resilience phenomenon and cell viability constraints. The accuracy of optimization results depends heavily on the development of essential kinetic models of metabolic networks. Kinetic models can quantitatively capture the experimentally observed regulation data of metabolic systems and are often used to find the optimal manipulation of external inputs. To address the issues of optimizing the regulatory structure of metabolic networks, it is necessary to consider qualitative effects, e.g., the resilience phenomena and cell viability constraints. Combining the qualitative and quantitative descriptions for metabolic networks makes it possible to design a viable strain and accurately predict the maximum possible flux rates of desired products. Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. Two case studies will present in the conference to illustrate the phenomena.

Keywords: Fuzzy multi-objective optimization problem, kinetic model, metabolic engineering.

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Authors: Dong Hun Kim, Won Kyu Lee, Sok Won Kim

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Small cracks or chips of a product appear very frequently in the course of continuous production of an automatic press process system. These phenomena become the cause of not only defective product but also damage of a press mold. In order to solve this problem AE system was introduced. AE system was expected to be very effective to real time detection of the defective product and to prevention of the damage of the press molds. In this study, for pick and analysis of AE signals generated from the press process, AE sensors/pre-amplifier/analysis and processing board were used as frequently found in the other similar cases. For analysis and processing the AE signals picked in real time from the good or bad products, specialized software called cdm8 was used. As a result of this work it was conformed that intensity and shape of the various AE signals differ depending on the weight and thickness of metal sheet and process type.

Keywords: press, acoustic emission, signal processing

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Authors: Iliana Marinova, Valentin Mateev

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For investigations of electromagnetic field distributions in biological structures by Finite Element Method (FEM), a method for automatic 3D model building of human anatomical objects is developed. Models are made by meshed structures and specific electromagnetic material properties for each tissue type. Mesh is built according to specific FEM criteria for achieving good solution accuracy. Several FEM models of anatomical objects are built. Formulation using magnetic vector potential and scalar electric potential (A-V, A) is used for modeling of electromagnetic fields in human tissue objects. The developed models are suitable for investigations of electromagnetic field distributions in human tissues exposed in external fields during magnetic stimulation, defibrillation, impedance tomography etc.

Keywords: electromagnetic field, finite element method, humantissue.

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Authors: A. K. Prajapati

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In this paper, the periodic surveillance scheme has been proposed for any convex region using mobile wireless sensor nodes. A sensor network typically consists of fixed number of sensor nodes which report the measurements of sensed data such as temperature, pressure, humidity, etc., of its immediate proximity (the area within its sensing range). For the purpose of sensing an area of interest, there are adequate number of fixed sensor nodes required to cover the entire region of interest. It implies that the number of fixed sensor nodes required to cover a given area will depend on the sensing range of the sensor as well as deployment strategies employed. It is assumed that the sensors to be mobile within the region of surveillance, can be mounted on moving bodies like robots or vehicle. Therefore, in our scheme, the surveillance time period determines the number of sensor nodes required to be deployed in the region of interest. The proposed scheme comprises of three algorithms namely: Hexagonalization, Clustering, and Scheduling, The first algorithm partitions the coverage area into fixed sized hexagons that approximate the sensing range (cell) of individual sensor node. The clustering algorithm groups the cells into clusters, each of which will be covered by a single sensor node. The later determines a schedule for each sensor to serve its respective cluster. Each sensor node traverses all the cells belonging to the cluster assigned to it by oscillating between the first and the last cell for the duration of its life time. Simulation results show that our scheme provides full coverage within a given period of time using few sensors with minimum movement, less power consumption, and relatively less infrastructure cost.

Keywords: Sensor Network, Graph Theory, MSN, Communication.

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the supportvectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: Discrete Wavelet Transform, Electroencephalogram, Pattern Recognition, Principal Component Analysis, Support Vector Machine.

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Authors: T. Abdulkadir

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Most often, marking and assessment of multiple choice kinds of examinations have been opined by many as a cumbersome and herculean task to accomplished manually in Nigeria. Usually this may be in obvious nexus to the fact that mass numbers of candidates were known to take the same examination simultaneously. Eventually, marking such a mammoth number of booklets dared and dread even the fastest paid examiners who often undertake the job with the resulting consequences of stress and boredom. This paper explores the evolution, as well as the set aim to envision and transcend marking the Multiple Choice Objectives- type examination into a thing of creative recreation, or perhaps a more relaxing activity via the use of the mobile phone. A more “pragmatic” dimension method was employed to achieve this work, rather than the formal “in-depth research” based approach due to the “novelty” of the mobile-smartphone e-Marking Scheme discovery. Moreover, being an evolutionary scheme, no recent academic work shares a direct same topic concept with the ‘use of cell phone as an e-marking technique’ was found online; thus, the dearth of even miscellaneous citations in this work. Additional future advancements are what steered the anticipatory motive of this paper which laid the fundamental proposition. However, the paper introduces for the first time the concept of mobile-smart phone e-marking, the steps to achieve it, as well as the merits and demerits of the technique all spelt out in the subsequent pages.

Keywords: Cell phone, e-marking scheme, mobile phone, mobile-smart phone, multiple choice objectives, smartphone.

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Authors: Salvatore L., Pires B., Campos M. C. M., De Souza Jr M. B.

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It is estimated that the total cost of abnormal conditions to US process industries is around $20 billion dollars in annual losses. The hydrotreatment (HDT) of diesel fuel in petroleum refineries is a conversion process that leads to high profitable economical returns. However, this is a difficult process to control because it is operated continuously, with high hydrogen pressures and it is also subject to disturbances in feed properties and catalyst performance. So, the automatic detection of fault and diagnosis plays an important role in this context. In this work, a hybrid approach based on neural networks together with a pos-processing classification algorithm is used to detect faults in a simulated HDT unit. Nine classes (8 faults and the normal operation) were correctly classified using the proposed approach in a maximum time of 5 minutes, based on on-line data process measurements.

Keywords: Fault detection, hydrotreatment, hybrid systems, neural networks.

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Authors: Iryna Atamaniuk, Hannah Boysen, Nils Wieczorek, Natalia Politaeva, Iuliia Bazarnova, Kerstin Kuchta

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Over the last decade due to climate change and a strategy of natural resources preservation, the interest for the aquatic biomass has dramatically increased. Along with mitigation of the environmental pressure and connection of waste streams (including CO₂ and heat emissions), microalgae bioeconomy can supply food, feed, as well as the pharmaceutical and power industry with number of value-added products. Furthermore, in comparison to conventional biomass, microalgae can be cultivated in wide range of conditions without compromising food and feed production, thus addressing issues associated with negative social and the environmental impacts. This paper presents the state-of-the art technology for microalgae bioeconomy from cultivation process to production of valuable components and by-streams. Microalgae Chlorella sorokiniana were cultivated in the pilot-scale innovation concept in Hamburg (Germany) using different systems such as race way pond (5000 L) and flat panel reactors (8 x 180 L). In order to achieve the optimum growth conditions along with suitable cellular composition for the further extraction of the value-added components, process parameters such as light intensity, temperature and pH are continuously being monitored. On the other hand, metabolic needs in nutrients were provided by addition of micro- and macro-nutrients into a medium to ensure autotrophic growth conditions of microalgae. The cultivation was further followed by downstream process and extraction of lipids, proteins and saccharides. Lipids extraction is conducted in repeated-batch semi-automatic mode using hot extraction method according to Randall. As solvents hexane and ethanol are used at different ratio of 9:1 and 1:9, respectively. Depending on cell disruption method along with solvents ratio, the total lipids content showed significant variations between 8.1% and 13.9 %. The highest percentage of extracted biomass was reached with a sample pretreated with microwave digestion using 90% of hexane and 10% of ethanol as solvents. Proteins content in microalgae was determined by two different methods, namely: Total Kejadahl Nitrogen (TKN), which further was converted to protein content, as well as Bradford method using Brilliant Blue G-250 dye. Obtained results, showed a good correlation between both methods with protein content being in the range of 39.8–47.1%. Characterization of neutral and acid saccharides from microalgae was conducted by phenol-sulfuric acid method at two wavelengths of 480 nm and 490 nm. The average concentration of neutral and acid saccharides under the optimal cultivation conditions was 19.5% and 26.1%, respectively. Subsequently, biomass residues are used as substrate for anaerobic digestion on the laboratory-scale. The methane concentration, which was measured on the daily bases, showed some variations for different samples after extraction steps but was in the range between 48% and 55%. CO₂ which is formed during the fermentation process and after the combustion in the Combined Heat and Power unit can potentially be used within the cultivation process as a carbon source for the photoautotrophic synthesis of biomass.

Keywords: Bioeconomy, lipids, microalgae, proteins, saccharides.

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Authors: E. Supriyanto, S. C. Seow

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Early Intervention Program (EIP) is required to improve the overall development of children with Trisomy 21 (Down syndrome). In order to help trainer and parent in the implementation of EIP, a support system has been developed. The support system is able to screen data automatically, store and analyze data, generate individual EIP (curriculum) with optimal training duration and to generate training automatically. The system consists of hardware and software where the software has been implemented using Java language and Linux Fedora. The software has been tested to ensure the functionality and reliability. The prototype has been also tested in Down syndrome centers. Test result shows that the system is reliable to be used for generation of an individual curriculum which includes the training program to improve the motor, cognitive, and combination abilities of Down syndrome children under 6 years.

Keywords: Early intervention program (curriculum), Trisomy21, support system, Java.

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Authors: Mohammed S. Assiri

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In EFL programs, rating scales used in writing assessment are often constructed by intuition. Intuition-based scales tend to provide inaccurate and divisive ratings of learners’ writing performance. Hence, following an empirical approach, this study attempted to develop a rating scale for elementary-level writing at an EFL program in Saudi Arabia. Towards this goal, 98 students’ essays were scored and then coded using comprehensive taxonomy of writing constructs and their measures. An automatic linear modeling was run to find out which measures would best predict essay scores. A nonparametric ANOVA, the Kruskal-Wallis test, was then used to determine which measures could best differentiate among scoring levels. Findings indicated that there were certain measures that could serve as either good predictors of essay scores or differentiators among scoring levels, or both. The main conclusion was that a rating scale can be empirically developed using predictive and discriminative statistical tests.

Keywords: Analytic scoring, rating scales, writing assessment, writing performance.

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Authors: R. Amandi, A. Shahbazi, A. Mohebi, M. Bazargan, Y. Jaberi, P. Emadi, A. Valizade

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The application of Neural Network for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. In our work, tachycardia features obtained are used for the training and testing of a Neural Network. In this study we are using Fuzzy Probabilistic Neural Networks as an automatic technique for ECG signal analysis. As every real signal recorded by the equipment can have different artifacts, we needed to do some preprocessing steps before feeding it to our system. Wavelet transform is used for extracting the morphological parameters of the ECG signal. The outcome of the approach for the variety of arrhythmias shows the represented approach is superior than prior presented algorithms with an average accuracy of about %95 for more than 7 tachy arrhythmias.

Keywords: Fuzzy Logic, Probabilistic Neural Network, Tachycardia, Wavelet Transform.

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Authors: Yingjun Ruan, Qingrong Liu, Weiguo Zhou, Toshiyuki Watanabe

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This paper has examined the energy consumption characteristics in six different buildings including apartments, offices, commercial buildings, hospitals, hotels and educational facilities. Then 5-hectare (50000m2) development site for respective building-s type has been assumed as case study to evaluate the introduction effect of Combined Heat and Power (CHP). All kinds of CHP systems with different distributed generation technologies including Gas Turbine (GT), Gas Engine (GE), Diesel Engine (DE), Solid Oxide Fuel Cell (SOFC) and Polymer Electrolyte Fuel Cell (PEFC), have been simulated by using HEATMAP, CHP system analysis software. And their primary energy utilization efficiency, energy saving ratio and CO2 reduction ratio have evaluated and compared respectively. The results can be summarized as follows: Various buildings have their special heat to power ratio characteristics. Matching the heat to power ratio demanded from an individual building with that supplied from a CHP system is very important. It is necessary to select a reasonable distributed generation technologies according to the load characteristics of various buildings. Distributed generation technologies with high energy generating efficiency and low heat to power ratio, like SOFC and PEFC is more reasonable selection for Building Combined Heat and Power (BCHP). CHP system is an attractive option for hotels, hospitals and apartments in Japan. The users can achieve high energy saving and environmental benefit by introducing a CHP systems. In others buildings, especially like commercial buildings and offices, the introduction of CHP system is unreasonable.

Keywords: Combined heat and power, distributed generation technologies, heat-tao-power ratio, energy saving ratio, CO2 reduction ratio

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Authors: А. Pashayev, D. Askerov, R. Sadiqov, A. Samedov, C. Ardil

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In contrast to existing methods which do not take into account multiconnectivity in a broad sense of this term, we develop mathematical models and highly effective combination (BIEM and FDM) numerical methods of calculation of stationary and quasistationary temperature field of a profile part of a blade with convective cooling (from the point of view of realization on PC). The theoretical substantiation of these methods is proved by appropriate theorems. For it, converging quadrature processes have been developed and the estimations of errors in the terms of A.Ziqmound continuity modules have been received. For visualization of profiles are used: the method of the least squares with automatic conjecture, device spline, smooth replenishment and neural nets. Boundary conditions of heat exchange are determined from the solution of the corresponding integral equations and empirical relationships. The reliability of designed methods is proved by calculation and experimental investigations heat and hydraulic characteristics of the gas turbine first stage nozzle blade.

Keywords: Modeling, Simulating, Gas Turbine, Cooled Blades.

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Authors: Yue Wang, Jixin Ma, Brian Knight

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This paper presents a visualized computer aided case tool for non-expert, called Visual Time, for representing and reasoning about incomplete and uncertain temporal information. It is both expressive and versatile, allowing logical conjunctions and disjunctions of both absolute and relative temporal relations, such as “Before”, “Meets”, “Overlaps”, “Starts”, “During”, and “Finishes”, etc. In terms of a visualized framework, Visual Time provides a user-friendly environment for describing scenarios with rich temporal structure in natural language, which can be formatted as structured temporal phrases and modeled in terms of Temporal Relationship Diagrams (TRD). A TRD can be automatically and visually transformed into a corresponding Time Graph, supported by automatic consistency checker that derives a verdict to confirm if a given scenario is temporally consistent or inconsistent.

Keywords: Time Visualization, Uncertainty, Incompleteness, Consistency Checking.

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Authors: Rauza Sukma Rita, Katsuya Dezaki, Yuko Maejima, Toshihiko Yada

Abstract:

Oxytocin is a nine-amino acid peptide synthesized in the paraventricular nucleus (PVN) and supraoptic nucleus (SON) of the hypothalamus. Oxytocin promotes contraction of the uterus during birth and milk ejection during breast feeding. Although oxytocin receptors are found predominantly in the breasts and uterus of females, many tissues and organs express oxytocin receptors, including the pituitary, heart, kidney, thymus, vascular endothelium, adipocytes, osteoblasts, adrenal gland, pancreatic islets, and many cell lines. On the other hand, in pancreatic islets, oxytocin receptors are expressed in both α-cells and β-cells with stronger expression in α- cells. However, to our knowledge there are no reports yet about the effect of oxytocin on cytosolic calcium reaction on α and β-cell. This study aims to investigate the effect of oxytocin on α-cells and β-cells and its oscillation pattern. Islet of Langerhans from wild type mice were isolated by collagenase digestion. Isolated and dissociated single cells either α-cells or β-cells on coverslips were mounted in an open chamber and superfused in HKRB. Cytosolic concentration ([Ca2+]i) in single cells were measured by fura-2 microfluorimetry. After measurement of [Ca2+]i, α-cells were identified by subsequent immunocytochemical staining using an anti-glucagon antiserum. In β-cells, the [Ca2+]i increase in response to oxytocin was observed only under 8.3 mM glucose condition, whereas in α-cells, [Ca2+]i an increase induced by oxytocin was observed in both 2.8 mM and 8.3 mM glucose. The oscillation incidence was induced more frequently in β-cells compared to α-cells. In conclusion, the present study demonstrated that oxytocin directly interacts with both α-cells and β-cells and induces increase of [Ca2+]i and its specific patterns.

Keywords: α-cells, β-cells, cytosolic calcium concentration, oscillation, oxytocin.

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Authors: Osman Mohammed Hegazy, Ali Hamed El Bastawissy, Romani Farid Ibrahim

Abstract:

In this paper, our concern is the management of mobile transactions in the shared area among many servers, when the mobile user moves from one cell to another in online partiallyreplicated distributed mobile database environment. We defined the concept of transaction and classified the different types of transactions. Based on this analysis, we propose an algorithm that handles the disconnection due to moving among sites.

Keywords: Concurrency, mobile database, transaction processing, two phase locking.

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Authors: Jung-Eun Park, Kyung-Whan Oh

Abstract:

Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.

Keywords: Intelligent Clustering, Multi-Agent System, PCA, SOM, VC(Variance Criterion)

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Authors: Shuo-Li Hsu, Peng-Wei Gau, I-Lin Wu, Jyh-Horng Jeng

Abstract:

For most image fusion algorithms separate relationship by pixels in the image and treat them more or less independently. In addition, they have to be adjusted different parameters in different time or weather. In this paper, we propose a region–based image fusion which combines aspects of feature and pixel-level fusion method to replace only by pixel. The basic idea is to segment far infrared image only and to add information of each region from segmented image to visual image respectively. Then we determine different fused parameters according different region. At last, we adopt artificial neural network to deal with the problems of different time or weather, because the relationship between fused parameters and image features are nonlinear. It render the fused parameters can be produce automatically according different states. The experimental results present the method we proposed indeed have good adaptive capacity with automatic determined fused parameters. And the architecture can be used for lots of applications.

Keywords: Image fusion, Region-based fusion, Segmentation, Neural network, Multi-sensor.

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