Search results for: Brain activities

Authors: Gaya Tridinanti

Abstract:

Enhanching English vocabulary in early childhood is the main problem often faced by teachers. Thus, the purpose of this study was to determine the enhancement of children’s English vocabulary acquisition by using digital storytelling. This type of research was an action research. It consisted of a series of four activities done in repeated cycles: planning, implementation, observation, and reflection. The subject of the study consisted of 30 students of B group (5-6 years old) attending Happy Kids Kindergarten Palembang, Indonesia. This research was conducted in three cycles. The methods used for data collection were observation and documentation. Descriptive qualitative and quantitative methods were also used to analyse the data. The research showed that the digital storytelling learning activities could enhance the children’s English vocabulary acquisition. It is based on the data in which the enhancement in pre-cycle was 37% and 51% in Cycle I. In Cycle II it was 71% and in Cycle III it was 89.3%. The results showed an enhancement of about 14% from the pre-cycle to Cycle I, 20% from Cycle I to Cycle II, and enhancement of about 18.3% from Cycle II to Cycle III. The conclusion of this study suggests that digital storytelling learning method could enhance the English vocabulary acquisition of B group children at the Happy Kids Kindergarten Palembang. Therefore, digital storytelling can be considered as an alternative to improve English language learning in the classroom.

Keywords: Acquisition, enhancing, digital storytelling, English vocabulary.

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Authors: Miguel Angel Garcia-Ruiz, Pedro Cesar Santana-Mancilla, Laura Sanely Gaytan-Lugo

Abstract:

This paper describes the design and application of a short in-class project conducted in Algoma University’s Human-Computer Interaction (HCI) course taught at the Bachelor of Computer Science. The project was based on the Maker Movement (people using and reusing electronic components and everyday materials to tinker with technology and make interactive applications), where students applied low-cost and easy-to-use electronic components, the Arduino Uno microcontroller board, software tools, and everyday objects. Students collaborated in small teams by completing hands-on activities with them, making an interactive walking cane for blind people. At the end of the course, students filled out a Technology Acceptance Model version 2 (TAM2) questionnaire where they evaluated microcontroller boards’ applications in HCI classes. We also asked them about applying the Maker Movement in HCI classes. Results showed overall students’ positive opinions and response about using microcontroller boards in HCI classes. We strongly suggest that every HCI course should include practical activities related to tinkering with technology such as applying microcontroller boards, where students actively and constructively participate in teams for achieving learning objectives.

Keywords: Maker movement, microcontrollers, learning, projects, course, technology acceptance.

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Authors: Ramaswamy Palaniappan

Abstract:

In this paper, a second order autoregressive (AR) model is proposed to discriminate alcoholics using single trial gamma band Visual Evoked Potential (VEP) signals using 3 different classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN), Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear Discriminant (LD). Electroencephalogram (EEG) signals were recorded from alcoholic and control subjects during the presentation of visuals from Snodgrass and Vanderwart picture set. Single trial VEP signals were extracted from EEG signals using Elliptic filtering in the gamma band spectral range. A second order AR model was used as gamma band VEP exhibits pseudo-periodic behaviour and second order AR is optimal to represent this behaviour. This circumvents the requirement of having to use some criteria to choose the correct order. The averaged discrimination errors of 2.6%, 2.8% and 11.9% were given by LD, MLP-BP and SFA classifiers. The high LD discrimination results show the validity of the proposed method to discriminate between alcoholic subjects.

Keywords: Linear Discriminant, Neural Network, VisualEvoked Potential.

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Authors: Helena Dvořáčková, Jakub Elbl, Irina Mikajlo, Antonín Kintl, Jaroslav Hynšt, Olga Urbánková, Jaroslav Záhora

Abstract:

Application of biochar to arable soils represents a new approach to restore soil health and quality. Many studies reported the positive effect of biochar application on soil fertility and development of soil microbial community. Moreover biochar may affect the soil water retention, but this effect has not been sufficiently described yet. Therefore this study deals with the influence of biochar application on: microbial activities in soil, availability of mineral nitrogen in soil for microorganisms, mineral nitrogen retention and plant production. To demonstrate the effect of biochar addition on the above parameters, the pot experiment was realized. As a model crop, Lactuca sativa L. was used and cultivated from December 10th 2014 till March 22th 2015 in climate chamber in thoroughly homogenized arable soil with and without addition of biochar. Five variants of experiment (V1 – V5) with different regime of irrigation were prepared. Variants V1 – V2 were fertilized by mineral nitrogen, V3 – V4 by biochar and V5 was a control. The significant differences were found only in plant production and mineral nitrogen retention. The highest content of mineral nitrogen in soil was detected in V1 and V2, about 250 % in comparison with the other variants. The positive effect of biochar application on soil fertility, mineral nitrogen availability was not found. On the other hand results of plant production indicate the possible positive effect of biochar application on soil water retention.

Keywords: Arable soil, biochar, drought, mineral Nitrogen.

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Authors: Joshua N. Edokpayi, John O. Odiyo, Patience P. Shikwambana

Abstract:

Water is a very rare natural resource in South Africa. Ga-Selati River is used for both domestic and industrial purposes. This study was carried out in order to assess the quality of Ga-Selati River in a mining area of Limpopo Province-Phalaborwa. The pH, Electrical Conductivity (EC) and Total Dissolved Solids (TDS) were determined using a Crinson multimeter while turbidity was measured using a Labcon Turbidimeter. The concentrations of Al, Ca, Cd, Cr, Fe, K, Mg, Mn, Na and Pb were analysed in triplicate using a Varian 520 flame atomic absorption spectrometer (AAS) supplied by PerkinElmer, after acid digestion with nitric acid in a fume cupboard. The average pH of the river from eight different sampling sites was 8.00 and 9.38 in wet and dry season respectively. Higher EC values were determined in the dry season (138.7 mS/m) than in the wet season (96.93 mS/m). Similarly, TDS values were higher in dry (929.29 mg/L) than in the wet season (640.72 mg/L) season. These values exceeded the recommended guideline of South Africa Department of Water Affairs and Forestry (DWAF) for domestic water use (70 mS/m) and that of the World Health Organization (WHO) (600 mS/m), respectively. Turbidity varied between 1.78-5.20 and 0.95-2.37 NTU in both wet and dry seasons. Total hardness of 312.50 mg/L and 297.75 mg/L as the concentration of CaCO₃ was computed for the river in both the wet and the dry seasons and the river water was categorised as very hard. Mean concentration of the metals studied in both the wet and the dry seasons are: Na (94.06 mg/L and 196.3 mg/L), K (11.79 mg/L and 13.62 mg/L), Ca (45.60 mg/L and 41.30 mg/L), Mg (48.41 mg/L and 44.71 mg/L), Al (0.31 mg/L and 0.38 mg/L), Cd (0.01 mg/L and 0.01 mg/L), Cr (0.02 mg/L and 0.09 mg/L), Pb (0.05 mg/L and 0.06 mg/L), Mn (0.31 mg/L and 0.11 mg/L) and Fe (0.76 mg/L and 0.69 mg/L). Results from this study reveal that most of the metals were present in concentrations higher than the recommended guidelines of DWAF and WHO for domestic use and the protection of aquatic life.

Keywords: Contamination, mining activities, surface water, trace metals.

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Authors: Abdelatief S. H. El-Jasser

Abstract:

The aim of the present study was to investigate the chemical and biological properties of local cowpea seed protein cultivated in Gizan region. The results showed that the cowpea and its products contain high level of protein (22.9-77.6%), high carbohydrates (9.4-64.3%) and low fats (0.1-0.3%). The trypsin and chymotrypsin activities were found to be 32.2 and 15.2 units, respectively. These activities were not affected in both defatted and protein concentrate whereas they were significantly reduced in isolated protein and cooked samples. The phytate content of cooked and concentrated cowpea samples varied from 0.25% -0.32%, respectively. Tannin content was found to be 0.4% and 0.23% for cooked and raw samples, respectively. The in vitro protein digestibility was very high in cowpea seeds (75.04-78.76%). The biological evaluation using rats showed that the group fed with animal feed containing casein gain more weight than those fed with that containing cowpea. However, the group fed with cooked cowpea gain more weight than those fed with uncooked cowpea. On the other hand, in vivo digestion showed high value (98.33%) among the group consumed casein compared to other groups those consumed cowpea contains feed. This could be attributed to low antinutritional factors in casein contains feed compared to those of cowpea contains feed because cooking significantly increased the digestion rate (80.8% to 83.5%) of cowpea contains feed. Furthermore, the biological evaluation was high (91.67%) of casein containing feed compared to that of cowpea containing feed (80.83%-87.5%). The net protein utilization (NPU) was higher (89.67%) in the group fed with casein containing feed than that of cowpea containing feed (56.33%-69.67%).

Keywords: Biological properties, Cowpea seed protein, Antinutritional factors, In vitro digestibility

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Authors: Abdelouahab Moussaoui, Nabila Ferahta, Victor Chen

Abstract:

The development of aid's systems for the medical diagnosis is not easy thing because of presence of inhomogeneities in the MRI, the variability of the data from a sequence to the other as well as of other different source distortions that accentuate this difficulty. A new automatic, contextual, adaptive and robust segmentation procedure by MRI brain tissue classification is described in this article. A first phase consists in estimating the density of probability of the data by the Parzen-Rozenblatt method. The classification procedure is completely automatic and doesn't make any assumptions nor on the clusters number nor on the prototypes of these clusters since these last are detected in an automatic manner by an operator of mathematical morphology called skeleton by influence zones detection (SKIZ). The problem of initialization of the prototypes as well as their number is transformed in an optimization problem; in more the procedure is adaptive since it takes in consideration the contextual information presents in every voxel by an adaptive and robust non parametric model by the Markov fields (MF). The number of bad classifications is reduced by the use of the criteria of MPM minimization (Maximum Posterior Marginal).

Keywords: Clustering, Automatic Classification, SKIZ, MarkovFields, Image segmentation, Maximum Posterior Marginal (MPM).

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Authors: J. Mehena, M. C. Adhikary

Abstract:

In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.

Keywords: Clustering, fuzzy C-means, image segmentation, MR images, multiple kernels.

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Authors: P. P. Chandrasekera, P. B. Hewavithana, S. Rosario, M. H. M. N. Hearth, D. M. R. D. Mirihella

Abstract:

Periventricular Leukomalacia (PVL) is a White Matter Injury (WMI) of preterm neonatal brain. Objectives of the study were to assess the neuro-developmental outcome at one year of age and to determine a good protocol of cranial ultrasonography to detect PVL. Two hundred and sixty four preterm neonates were included in the study. Series of cranial ultrasound scans were done by using a dedicated neonatal head probe 4-10 MHz of Logic e portable ultrasound scanner. Clinical history of seizures, abnormal head growth (hydrocephalus or microcephaly) and developmental milestones were assessed and neurological examinations were done until one year of age. Among live neonates, 57% who had cystic PVL (Grades 2 and 3) manifested as cerebral palsy. In conclusion cystic PVL has permanent neurological disabilities like cerebral palsy. Good protocol of real time cranial ultrasonography to detect PVL is to perform scans at least once a week until one month and at term (40 weeks of gestation).

Keywords: Cerebral palsy, cranial ultrasonography, Periventricular Leukomalacia (PVL), preterm neonates.

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Authors: K. Lisinchuk

Abstract:

An effective approach to the management of international educational mobility in regional universities with the purpose of increasing tourist activity in the region is considered.

Keywords: Export and import of tourist and educational services, international academic mobility, regional tourist activities.

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Authors: Ahmad M. Khalil, Ahmad M. Alshamali, Marwan H. Gagaa

Abstract:

We investigated oxidative DNA damage caused by radio frequency radiation using 8-oxo-7, 8-dihydro-2'- deoxyguanosine (8-oxodG) generated in mice tissues after exposure to 900 MHz mobile phone radio frequency in three independent experiments. The RF was generated by a Global System for Mobile Communication (GSM) signal generator. The radio frequency field was adjusted to 25 V/m. The whole body specific absorption rate (SAR) was 1.0 W/kg. Animals were exposed to this field for 30 min daily for 30 days. 24 h post-exposure, blood serum, brain and spleen were removed and DNA was isolated. Enzyme-linked immunosorbent assay (ELISA) was used to measure 8-oxodG concentration. All animals survived the whole experimental period. The body weight of animals did not change significantly at the end of the experiment. No statistically significant differences observed in the levels of oxidative stress. Our results are not in favor of the hypothesis that 900 MHz RF induces oxidative damage.

Keywords: Mice, Mobile phone radiation, oxidative stress, 8-oxo-7, 8-dihydro-2'-deoxyguanosine

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Authors: A. Golmohammadi, B. Jahandideh

Abstract:

One of the determinants of a firm-s prosperity is the customers- perceived service quality and satisfaction. While service quality is wide in scope, and consists of various dimensions, there may be differences in the relative importance of these dimensions in affecting customers- overall satisfaction of service quality. Identifying the relative rank of different dimensions of service quality is very important in that it can help managers to find out which service dimensions have a greater effect on customers- overall satisfaction. Such an insight will consequently lead to more effective resource allocation which will finally end in higher levels of customer satisfaction. This issue –despite its criticality- has not received enough attention so far. Therefore, using a sample of 240 bank customers in Iran, an artificial neural network is developed to address this gap in the literature. As customers- evaluation of service quality is a subjective process, artificial neural networks –as a brain metaphor- may appear to have a potentiality to model such a complicated process. Proposing a neural network which is able to predict the customers- overall satisfaction of service quality with a promising level of accuracy is the first contribution of this study. In addition, prioritizing the service quality dimensions in affecting customers- overall satisfaction –by using sensitivity analysis of neural network- is the second important finding of this paper.

Keywords: service quality, customer satisfaction, relativeimportance, artificial neural network.

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Authors: B. Likar, K. Širok

Abstract:

The paper presents a new method for efficient innovation process management. Even though the innovation management methods, tools and knowledge are well established and documented in literature, most of the companies still do not manage it efficiently. Especially in SMEs the front end of innovation - problem identification, idea creation and selection - is often not optimally performed. Our eMIPS methodology represents a sort of "umbrella methodology" - a well-defined set of procedures, which can be dynamically adapted to the concrete case in a company. In daily practice, various methods (e.g. for problem identification and idea creation) can be applied, depending on the company's needs. It is based on the proactive involvement of the company's employees supported by the appropriate methodology and external experts. The presented phases are performed via a mixture of face-to-face activities (workshops) and online (eLearning) activities taking place in eLearning Moodle environment and using other e-communication channels. One part of the outcomes is an identified set of opportunities and concrete solutions ready for implementation. The other also very important result is connected to innovation competences for the participating employees related with concrete tools and methods for idea management. In addition, the employees get a strong experience for dynamic, efficient and solution oriented managing of the invention process. The eMIPS also represents a way of establishing or improving the innovation culture in the organization. The first results in a pilot company showed excellent results regarding the motivation of participants and also as to the results achieved.

Keywords: Creativity, distance learning, front end, innovation, problem.

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Authors: Monday O. Ale, Adebukola A. Akintade, Olasunbo O. Orungbemi

Abstract:

Fermentation as an important operation in the processing of cocoa beans is now affected by the recent climate change across the globe. The major requirement for effective fermentation is the ability of the material used to retain sufficient heat for the required microbial activities. Apart from the effects of climate on the rate of heat retention, the materials used for fermentation plays an important role. Most Farmers still restrict fermentation activities to the use of traditional methods. Improving on cocoa fermentation in this era of climate change makes it necessary to work on other materials that can be suitable for cocoa fermentation. Therefore, the objective of this study was to determine the effects of fermentation techniques on the quality of cocoa beans. The materials used in this fermentation research were heap-leaves (traditional), stainless steel, plastic tin, plastic basket and wooden box. The period of fermentation varies from zero days to 10 days. Physical and chemical tests were carried out for variables in quality determination in the samples. The weight per bean varied from 1.0-1.2 g after drying across the samples and the major color of the dry beans observed was brown except with the samples from stainless steel. The moisture content varied from 5.5-7%. The mineral content and the heavy metals decreased with increase in the fermentation period. A wooden box can conclusively be used as an alternative to heap-leaves as there was no significant difference in the physical features of the samples fermented with the two methods. The use of a wooden box as an alternative for cocoa fermentation is therefore recommended for cocoa farmers.

Keywords: Effects, fermentation, fermentation materials, period, quality.

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Authors: Mohammad Rohani, Gholamali Shafabakhsh, Abdolhosein Haddad, Ehsan Asnaashari

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Visual simulation has emerged as a key planning tool in built environment because it enables architects, engineers and project managers to visualize construction process evolution before the project actual commences. This provides an efficient technology for reducing time and cost through planning and controlling resources, machines and materials. With the development of infrastructure projects and the massive civil constructions such as bridges, urban tunnels and highways as well as sensitivity of their construction operations, it is very necessary to apply proper planning methods. Implementation of visual techniques into management of construction projects can provide a fundamental foundation for projects with massive activities and duplicate items. So, the purpose of this paper is to develop visual simulation management techniques for infrastructure projects such as highways bridges by the use of Four-Dimensional Computer-Aided design Models. This project simulates operational assembly-line for Box-Girder Concrete Bridges which it would be able to optimize the sequence and interaction of project activities and on the other hand, it would minimize any unintended conflicts prior to project start. In this paper, after introducing the various planning methods by building information model and concrete bridges in highways, an executive case study is demonstrated and then a visual technique (4D CAD) will be applied for the case. In the final step, the user feedback for interacting by this system evaluated according to six criteria.

Keywords: 4D application area, Box-Girder concrete bridges, CAD model, visual planning.

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: Time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition.

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

Abstract:

The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: Information Gain (IG), Intrusion Detection System (IDS), K-means Clustering, Weka.

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Authors: A. Golmohammadi, B. Jahandideh

Abstract:

One of the determinants of a firm-s prosperity is the customers- perceived service quality and satisfaction. While service quality is wide in scope, and consists of various dimensions, there may be differences in the relative importance of these dimensions in affecting customers- overall satisfaction of service quality. Identifying the relative rank of different dimensions of service quality is very important in that it can help managers to find out which service dimensions have a greater effect on customers- overall satisfaction. Such an insight will consequently lead to more effective resource allocation which will finally end in higher levels of customer satisfaction. This issue – despite its criticality- has not received enough attention so far. Therefore, using a sample of 240 bank customers in Iran, an artificial neural network is developed to address this gap in the literature. As customers- evaluation of service quality is a subjective process, artificial neural networks –as a brain metaphor- may appear to have a potentiality to model such a complicated process. Proposing a neural network which is able to predict the customers- overall satisfaction of service quality with a promising level of accuracy is the first contribution of this study. In addition, prioritizing the service quality dimensions in affecting customers- overall satisfaction –by using sensitivity analysis of neural network- is the second important finding of this paper.

Keywords: service quality, customer satisfaction, relative importance, artificial neural network.

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Authors: A. M. Gadanya, M. S. Sule, M. K. Atiku

Abstract:

Effect of oral administration of “Gadagi" tea on liver function was assessed on 50 healthy male albino rats which were grouped and administered with different doses(mg/kg) i.e low dose (380mg/kg, 415mg/kg, 365mg/kg, 315mg/kg for “sak", “sada" and “magani" respectively), standard dose ( 760mg/kg, 830mg/kg, 730mg/kg for “sak-, “sada" and “magani" respectively) and high dose (1500mg/kg, 1700mg/kg and 1460mg/kg for “sak--,"sada" and “magani" groups respectively) for a period of four weeks. Animals that were not administered with the tea constituted the control group. At the end of fourth week, the animals were sacrificed and their serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total protein (TP), albumin (ALB), and globulins (GLO) were determined. Mean serum ALT and ALP activities were significantly higher (P<0.05) in rats orally administered with high dose of “sak" and those administered with standard dose of “sada" than those of the control group, suggesting a probable impairment of liver function due to liver cytolysis.Mean serum AST, ALT and ALP activities were significantly lower (P<0.05) in rats that were orally administered with high dose of “magani" than that of the control group, suggesting a probable improvement in liver function (due to decrease in liver cytolysis). Mean serum TP, ALB and GLO levels were significantly higher (P<0.05) in rats that were orally administered with the various doses of“sak", “sada" and “magani" than those of the control group. This also suggests a probable improvement in the synthetic function of the liver.Thus, some dosages of “sak" and “sada could be hepatotoxic, whereas “magani" especially at the high dose administered could have pharmacologically positive effect on the liver of the rats.

Keywords: Gadagi" tea, Liver function, Oral, Rats.

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Authors: Mohamed K. Watfa

Abstract:

Like any sentient organism, a smart environment relies first and foremost on sensory data captured from the real world. The sensory data come from sensor nodes of different modalities deployed on different locations forming a Wireless Sensor Network (WSN). Embedding smart sensors in humans has been a research challenge due to the limitations imposed by these sensors from computational capabilities to limited power. In this paper, we first propose a practical WSN application that will enable blind people to see what their neighboring partners can see. The challenge is that the actual mapping between the input images to brain pattern is too complex and not well understood. We also study the connectivity problem in 3D/2D wireless sensor networks and propose distributed efficient algorithms to accomplish the required connectivity of the system. We provide a new connectivity algorithm CDCA to connect disconnected parts of a network using cooperative diversity. Through simulations, we analyze the connectivity gains and energy savings provided by this novel form of cooperative diversity in WSNs.

Keywords: Wireless Sensor Networks, Pervasive Computing, Eye Vision Application, 3D Connectivity, Clusters, Energy Efficient, Cooperative diversity.

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Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

Abstract:

Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: Memory, planning navigational ability, virtual reality.

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: Chin-Yu Lee, Iau-Teh Wang

Abstract:

The mountain road slope failures triggered by earthquake activities and torrential rain namely to create the disaster. Province Road No. 24 is a main route to the Wutai Township. The area of the study is located at the mileages between 46K and 47K along the road. However, the road has been suffered frequent damages as a result of landslide and slope failures during typhoon seasons. An understanding of the sliding behaviors in the area appears to be necessary. Slope failures triggered by earthquake activities and heavy rainfalls occur frequently. The study is to understand the mechanism of slope failures and to look for the way to deal with the situation. In order to achieve these objectives, this paper is based on theoretical and structural geology data interpretation program to assess the potential slope sliding behavior. The study showed an intimate relationship between the landslide behavior of the slopes and the stratum materials, based on structural geology analysis method to analysis slope stability and finds the slope safety coefficient to predict the sites of destroyed layer. According to the case study and parameter analyses results, the slope mainly slips direction compared to the site located in the southeast area. Find rainfall to result in the rise of groundwater level is main reason of the landslide mechanism. Future need to set up effective horizontal drain at corrective location, that can effective restrain mountain road slope failures and increase stability of slope.

Keywords: slope stability analysis, Stereographic Projection, wedge Failure.

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Authors: R. Tamborrino, F. Rinaudo

Abstract:

Digital technologies offer possibilities to effectively convert historical archives into instruments of knowledge able to provide a guide for the interpretation of historical phenomena. Digital conversion and management of those documents allow the possibility to add other sources in a unique and coherent model that permits the intersection of different data able to open new interpretations and understandings. Urban history uses, among other sources, the inventories that register human activities in a specific space (e.g. cadastres, censuses, etc.). The geographic localisation of that information inside cartographic supports allows for the comprehension and visualisation of specific relationships between different historical realities registering both the urban space and the peoples living there. These links that merge the different nature of data and documentation through a new organisation of the information can suggest a new interpretation of other related events. In all these kinds of analysis, the use of GIS platforms today represents the most appropriate answer. The design of the related databases is the key to realise the ad-hoc instrument to facilitate the analysis and the intersection of data of different origins. Moreover, GIS has become the digital platform where it is possible to add other kinds of data visualisation. This research deals with the industrial development of Turin at the beginning of the 20th century. A census of factories realized just prior to WWI provides the opportunity to test the potentialities of GIS platforms for the analysis of urban landscape modifications during the first industrial development of the town. The inventory includes data about location, activities, and people. GIS is shaped in a creative way linking different sources and digital systems aiming to create a new type of platform conceived as an interface integrating different kinds of data visualisation. The data processing allows linking this information to an urban space, and also visualising the growth of the city at that time. The sources, related to the urban landscape development in that period, are of a different nature. The emerging necessity to build, enlarge, modify and join different buildings to boost the industrial activities, according to their fast development, is recorded by different official permissions delivered by the municipality and now stored in the Historical Archive of the Municipality of Turin. Those documents, which are reports and drawings, contain numerous data on the buildings themselves, including the block where the plot is located, the district, and the people involved such as the owner, the investor, and the engineer or architect designing the industrial building. All these collected data offer the possibility to firstly re-build the process of change of the urban landscape by using GIS and 3D modelling technologies thanks to the access to the drawings (2D plans, sections and elevations) that show the previous and the planned situation. Furthermore, they access information for different queries of the linked dataset that could be useful for different research and targets such as economics, biographical, architectural, or demographical. By superimposing a layer of the present city, the past meets to the present-industrial heritage, and people meet urban history.

Keywords: Digital urban history, census, digitalisation, GIS, modelling, digital humanities.

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Authors: Maziar A. Sharbafi, Caro Lucas, Aida Mohammadinejad, Mostafa Yaghobi

Abstract:

The Combination of path planning and path following is the main purpose of this paper. This paper describes the developed practical approach to motion control of the MRL small size robots. An intelligent controller is applied to control omni-directional robots motion in simulation and real environment respectively. The Brain Emotional Learning Based Intelligent Controller (BELBIC), based on LQR control is adopted for the omni-directional robots. The contribution of BELBIC in improving the control system performance is shown as application of the emotional learning in a real world problem. Optimizing of the control effort can be achieved in this method too. Next the implicit communication method is used to determine the high level strategies and coordination of the robots. Some simple rules besides using the environment as a memory to improve the coordination between agents make the robots' decision making system. With this simple algorithm our team manifests a desirable cooperation.

Keywords: multi-agent systems (MAS), Emotional learning, MIMO system, BELBIC, LQR, Communication via environment

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Authors: Mohamed Aymen Slim, Afef Abdelkrim, Mohamed Benrejeb

Abstract:

The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches.

Keywords: ElectroMyoGraphic (EMG) signals, Experimental approach, Handwriting process, Radial Basis Functions (RBF) neural networks, Velocity Modeling.

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Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

Abstract:

Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: Agent communication, introspective agent, isolation of agent, policy enforcement system.

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Authors: S. K. Aruna, S. Chitra

Abstract:

This research paper presents a framework for classifying Magnetic Resonance Imaging (MRI) images for Dementia. Dementia, an age-related cognitive decline is indicated by degeneration of cortical and sub-cortical structures. Characterizing morphological changes helps understand disease development and contributes to early prediction and prevention of the disease. Modelling, that captures the brain’s structural variability and which is valid in disease classification and interpretation is very challenging. Features are extracted using Gabor filter with 0, 30, 60, 90 orientations and Gray Level Co-occurrence Matrix (GLCM). It is proposed to normalize and fuse the features. Independent Component Analysis (ICA) selects features. Support Vector Machine (SVM) classifier with different kernels is evaluated, for efficiency to classify dementia. This study evaluates the presented framework using MRI images from OASIS dataset for identifying dementia. Results showed that the proposed feature fusion classifier achieves higher classification accuracy.

Keywords: Magnetic resonance imaging, dementia, Gabor filter, gray level co-occurrence matrix, support vector machine.

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Authors: Alicia Heraz, Claude Frasson

Abstract:

This paper investigates how the use of machine learning techniques can significantly predict the three major dimensions of learner-s emotions (pleasure, arousal and dominance) from brainwaves. This study has adopted an experimentation in which participants were exposed to a set of pictures from the International Affective Picture System (IAPS) while their electrical brain activity was recorded with an electroencephalogram (EEG). The pictures were already rated in a previous study via the affective rating system Self-Assessment Manikin (SAM) to assess the three dimensions of pleasure, arousal, and dominance. For each picture, we took the mean of these values for all subjects used in this previous study and associated them to the recorded brainwaves of the participants in our study. Correlation and regression analyses confirmed the hypothesis that brainwave measures could significantly predict emotional dimensions. This can be very useful in the case of impassive, taciturn or disabled learners. Standard classification techniques were used to assess the reliability of the automatic detection of learners- three major dimensions from the brainwaves. We discuss the results and the pertinence of such a method to assess learner-s emotions and integrate it into a brainwavesensing Intelligent Tutoring System.

Keywords: Algorithms, brainwaves, emotional dimensions, performance.

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Authors: Dimitrios Zarpalas, Anastasios Zafeiropoulos, Petros Daras, Nicos Maglaveras

Abstract:

Segmentation techniques based on Active Contour Models have been strongly benefited from the use of prior information during their evolution. Shape prior information is captured from a training set and is introduced in the optimization procedure to restrict the evolution into allowable shapes. In this way, the evolution converges onto regions even with weak boundaries. Although significant effort has been devoted on different ways of capturing and analyzing prior information, very little thought has been devoted on the way of combining image information with prior information. This paper focuses on a more natural way of incorporating the prior information in the level set framework. For proof of concept the method is applied on hippocampus segmentation in T1-MR images. Hippocampus segmentation is a very challenging task, due to the multivariate surrounding region and the missing boundary with the neighboring amygdala, whose intensities are identical. The proposed method, mimics the human segmentation way and thus shows enhancements in the segmentation accuracy.

Keywords: Medical imaging & processing, Brain MRI segmentation, hippocampus segmentation, hippocampus-amygdala missingboundary, weak boundary segmentation, region based segmentation, prior information, local weighting scheme in level sets, spatialdistribution of labels, gradient distribution on boundary.

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