Search results for: auditory error recognition

Authors: Yana Gorina, Olga Lopatina, Elina Tsigeman, Larisa Mararitsa

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The ability to act in concert with others, the so-called sensorimotor synchronisation, is a fundamental human ability that underlies successful interpersonal coordination. The manifestation of accuracy and plasticity in synchronisation is an adaptive aspect of interaction with the environment, as well as the ability to predict upcoming actions and behaviour of others. The ability to temporarily coordinate one’s actions with a predictable external event is manifested in such types of social behaviour as a synchronised group dance to music played live by an orchestra, group sports (rowing, swimming, etc.), synchronised actions of surgeons during an operation, applause from an admiring audience, walking rhythms, etc. Both our body and mind are involved in achieving the synchronisation during social interactions. However, it has not yet been well described how the brain determine the external rhythm and what neuropeptides coordinate and synchronise actions. Over the past few decades, there has been an increased interest among neuroscientists and neurophysiologists regarding the neuropeptide oxytocin in the context of its complex, diverse and sometimes polar effects manifested in the emotional and social aspects of behaviour (attachment, trust, empathy, emotion recognition, stress response, anxiety and depression, etc.). Presumable, oxytocin might also be involved in social synchronisation processes. The aim of our study is to test the hypothesis that oxytocin is linked to interpersonal synchronisation in a pair of strangers.

Keywords: behavior, movement, oxytocin, synchronization

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Authors: T. Pastraporn, J. Panida, P. Gasamapong, N. Jintana

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The study of inclusive educational environment of exceptional and normal children at the regional centre for special education aimed to establish guidelines for creating an environment for inclusive education. Buildings utilization of thirty-five elementary schools providing inclusive educational program in Bangkok were analyzed to study the following aspects: 1) The environment of exceptional and normal students’ inclusive classes at the regional centre for special education 2) The patterns of the environment suited to the exceptional and normal students’ inclusive classes 3) Environmental management policies for the inclusive classes of exceptional and normal students. Information was gathered from surveys, observations, questionnaires, document analysis, interviews, and non-experimental research. The findings showed that the usable spaces in school buildings were designated to enhance the three kinds of social learning experience: 1) Support class control 2) Help developing students’ personality consisting of physical, verbal and emotional expressions that are socially accepted 3) Recognition and learning, which are needed for the increasing of learning experience, were caused by having an interaction with the environment. Thus, the school buildings’ space designation positively affected the environmental management of exceptional and normal students’ inclusive classes.

Keywords: learning environment, inclusive education, school buildings, exceptional and normal children

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Authors: Kevin Spencer G. Anglim, Zhenyu Zhang, Qingbin Gao

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Reusable launch vehicles (RLVs) present a more environmentally-friendly approach to accessing space when compared to traditional launch vehicles that are discarded after each flight. This paper studies the recyclable nature of RLVs by presenting a solution method for determining minimum-fuel optimal trajectories using principles from optimal control theory and particle swarm optimization (PSO). This problem is formulated as a minimum-landing error powered descent problem where it is desired to move the RLV from a fixed set of initial conditions to three different sets of terminal conditions. However, unlike other powered descent studies, this paper considers the highly nonlinear effects caused by atmospheric drag, which are often ignored for studies on the Moon or on Mars. Rather than optimizing the controls directly, the throttle control is assumed to be bang-off-bang with a predetermined thrust direction for each phase of flight. The PSO method is verified in a one-dimensional comparison study, and it is then applied to the two-dimensional cases, the results of which are illustrated.

Keywords: minimum-fuel optimal trajectory, particle swarm optimization, reusable rocket, SpaceX

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Authors: Qi Wang

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Chinese has become a powerful worldwide language and millions of learners are studying it all over the words. Chinese is a tone language with unique meaningful characters, which makes foreign learners master it with more difficulties. On the other hand, as each foreign language, the learners of Chinese first will learn the basic Chinese Sound Structure (the initials and finals, tones, Neutral Tone and Tone Sandhi). It’s quite common that in the following studies, teachers made a lot of efforts on drilling and error correcting, in order to help students to pronounce correctly, but ignored the training of suprasegmental features (e.g. stress, intonation). This paper analysed the oral data based on our graduation students (two-year program) from 2006-2013, presents the intonation pattern of our graduates to speak Chinese as second language -high and plain with heavy accents, without lexical stress, appropriate stop endings and intonation, which led to the misunderstanding in different real contexts of communications and the international official Chinese test, e.g. HSK (Chinese Proficiency Test), HSKK (HSK Speaking Test). This paper also demonstrated how the Chinese to use the suprasegmental features strategically in different functions and moods (declarative, interrogative, imperative, exclamatory and rhetorical intonations) in order to train the learners to achieve better Communicative Performance.

Keywords: second language learning, suprasegmental, communication, HSK (Chinese Proficiency Test)

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Authors: Jenq-Shyong Chen, Ben-Fong Yu

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CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system.

Keywords: auto-tuning, CNC parameters, machining mode, high speed, high accuracy, high surface quality

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Authors: Merouane Salhi, Toufik Zebbiche, Omar Abada

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When the stagnation pressure of perfect gas increases, the specific heat and their ratio do not remain constant anymore and start to vary with this pressure. The gas does not remain perfect. Its state equation change and it becomes a real gas. In this case, the effects of molecular size and inter molecular attraction forces intervene to correct the state equation. The aim of this work is to show and discuss the effect of stagnation pressure on supersonic thermo dynamical, physical and geometrical flow parameters, to find a general case for real gas. With the assumptions that Berthelot’s state equation accounts for molecular size and inter molecular force effects, expressions are developed for analyzing supersonic flow for thermally and calorically imperfect gas lower than the dissociation molecules threshold. The designs parameters for supersonic nozzle like thrust coefficient depend directly on stagnation parameters of the combustion chamber. The application is for air. A computation of error is made in this case to give a limit of perfect gas model compared to real gas model.

Keywords: supersonic flow, real gas model, Berthelot’s state equation, Simpson’s method, condensation function, stagnation pressure

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Jamal Fayazi, Farhad Anoosheh, Mohammad R. Ghorbani, Ali R. Paydar

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In this study, to determine the best non-linear regression model describing the growth curve of native poultry, 9657 chicks of generations 18, 19, and 20 raised in Mazandaran breeding center were used. Fowls and roosters of this center distributed in south of Caspian Sea region. To estimate the genetic variability of none linear regression parameter of growth traits, a Gibbs sampling of Bayesian analysis was used. The average body weight traits in the first day (BW1), eighth week (BW8) and twelfth week (BW12) were respectively estimated as 36.05, 763.03, and 1194.98 grams. Based on the coefficient of determination, mean squares of error and Akaike information criteria, Gompertz model was selected as the best growth descriptive function. In Gompertz model, the estimated values for the parameters of maturity weight (A), integration constant (B) and maturity rate (K) were estimated to be 1734.4, 3.986, and 0.282, respectively. The direct heritability of BW1, BW8 and BW12 were respectively reported to be as 0.378, 0.3709, 0.316, 0.389, 0.43, 0.09 and 0.07. With regard to estimated parameters, the results of this study indicated that there is a possibility to improve some property of growth curve using appropriate selection programs.

Keywords: direct heritability, Gompertz, growth traits, maturity weight, native poultry

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Authors: Hoda Zolfagharnejad, Behnam Kamkar, Omid Abdi

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Crop coefficient (Kc) is an important factor contributing to estimation of evapotranspiration, and is also used to determine the irrigation schedule. This study investigated and determined the monthly Kc of winter wheat (Triticum aestivum L.) using five vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Soil Adjusted Vegetation Index (SAVI), Infrared Percentage Vegetation Index (IPVI), and Ratio Vegetation Index (RVI) of four basins in Golestan province, Iran. 14 Landsat-8 images according to crop growth stage were used to estimate monthly Kc of wheat. VIs were calculated based on infrared and near infrared bands of Landsat 8 images using Geographical Information System (GIS) software. The best VIs were chosen after establishing a regression relationship among these VIs with FAO Kc and Kc that was modified for the study area by the previous research based on R² and Root Mean Square Error (RMSE). The result showed that local modified SAVI with R²= 0.767 and RMSE= 0.174 was the best index to produce monthly wheat Kc maps.

Keywords: crop coefficient, remote sensing, vegetation indices, wheat

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Authors: Christine S. Veviani

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By observing the Brazilian labor reality, considered as degrading and oppressive, as well as responsible for creating obstacles to rights, this paper is aimed at demonstrating the obligatoriness of complying with the Constitution, as an effective instrument of the Democratic and Social State of Law established in the country since 1988, which identifies and determines the recognition of a single type of citizenship, as representation of equality, social inclusion and human dignity. To achieve this purpose, that is, to awake to a new culture focused on human respect / fundamental rights engraved in the Brazilian Constitution, doctrinal works, case law and labor courts (how they work) will be used as methodology. Thus, by concluding that there is a need for a change in behavior, by employers, intended to respect the Constitution, especially with regard to the concept and citizenship content if an attempt is made to achieve as a result few steps effectiveness of fundamental social rights protective of the Brazilian working class. Thus, by analyzing the Brazilian labor reality, the result is the employers' denial of full and single citizenship of workers, whose effects are directly related to the violation of rights, which leads to the conclusion that there is a need for a change in the behavior regarding the respect for the Constitution, especially concerning the effectiveness of fundamental social rights, which protect the working class in Brazil.

Keywords: employment relationships, opposing citizenships, constitutionalism, capitalism

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Authors: Atul Kumar, Veeranjaneyulu Gannedi, Qazi Naveed Ahmed

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Oligosaccharides, which are essential to all cellular organisms, play vital roles in cell recognition, signaling, and are involved in a broad range of biological processes. The chemical synthesis of carbohydrates represents a powerful tool to provide homogeneous glycans. In carbohydrate synthesis, the major concern is the orthogonal protection of hydroxyl groups that can be unmasked independently. Classical protecting groups include benzyl ethers (Bn), which are normally cleaved through hydrogenolysis or by means of metal reduction, and acetate (Ac), benzoate (Bz) or pivaloate esters, which are removed using base promoted hydrolysis. In present work a series of α-Oxoester (COBz) protected saccharides, with divergent base sensitivity profiles against benzoyl (Bz) and acetyl (Ac), were designed and KHSO₅/CH₃COCl in methanol was identified as an easy, mild, selective and efficient deprotecting reagent for their removal in the perspective of carbohydrate synthesis. Timely monitoring of later reagent was advantageous in establishing both sequential as well as simultaneous deprotecting of COBz, Bz, and Ac. The salient feature of our work is its ease to generate different acceptors using designed monosaccharides. In summary, we demonstrated α-Oxoester (COBz) as a new protecting group for carbohydrates and the application of this group for the synthesis of Glycosylphosphatidylinositol (GPI) anchor are in progress.

Keywords: α-Oxoester, oligosaccharides, new protecting group, acceptor synthesis, glycosylation

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Authors: Krzysztof Malczewski

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PET is widely applied scanning procedure in medical imaging based research. It delivers measurements of functioning in distinct areas of the human brain while the patient is comfortable, conscious and alert. This article presents the new compression sensing based super-resolution algorithm for improving the image resolution in clinical Positron Emission Tomography (PET) scanners. The issue of motion artifacts is well known in Positron Emission Tomography (PET) studies as its side effect. The PET images are being acquired over a limited period of time. As the patients cannot hold breath during the PET data gathering, spatial blurring and motion artefacts are the usual result. These may lead to wrong diagnosis. It is shown that the presented approach improves PET spatial resolution in cases when Compressed Sensing (CS) sequences are used. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. The application of CS to PET has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the goal is to combine super-resolution image enhancement algorithm with CS framework to achieve high resolution PET output. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity.

Keywords: PET, super-resolution, image reconstruction, pattern recognition

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Authors: Alaaddin Cerit, Suheyla Kocaman, Ulku Soydal

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During the past two decades, photoinitiated polymerization has been attracting a great interest in terms of scientific and industrial activity. The wide recognition of UV treatment in the polymer industry results not only from its many practical applications but also from its advantage for low-cost processes. Unlike most thermal curing systems, radiation-curable systems can polymerize at room temperature without additional heat, and the curing is completed in a very short time. The advantage of cationic UV technology is that post-cure can continue in the ‘dark’ after radiation. In this study, bio-based acrylated epoxidized soybean oil (AESO) was cured with UV radiation using radicalic photoinitiator Irgacure 184. Triarylsulphonium hexafluoroantimonate was used as cationic photoinitiator for curing of 3,4-epoxycyclohexylmethyl-3,4-epoxycyclohexanecarboxylate. The effect of curing time and the amount of initiators on the curing degree and thermal properties were investigated. The thermal properties of the coating were analyzed after crosslinking UV irradiation. The level of crosslinking in the coating was evaluated by FTIR analysis. Cationic UV-cured coatings demonstrated excellent adhesion and corrosion resistance properties. Therefore, our study holds a great potential with its simple and low-cost applications.

Keywords: acrylated epoxidized soybean oil, epoxy carboxylate, thermal properties, uv-curing

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Dauda A. Oladosu, Kamorudeen A Olaiya, Abdurahman Bello

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The deployment of smart meters by utility providers to gather fine grained spatiotemporal consumption data has grossly influenced the consumers’ emotion and behavior towards energy utilization. The quest for reduction in power consumption is now a subject of concern and one the methods adopted by the consumers to achieve this is Non-intrusive Load (appliance) Monitoring. Hence, this work presents performance Analysis of Domotics System as a tool for load monitoring when integrated with Consumer Control Unit of residential building. The system was developed with basic elements which enhance remote sensing, DTMF (Dual Tone Multi-frequency) recognition and cryptic messaging when specific task was performed. To demonstrate its applicability and suitability, this prototype was used consistently for six months at different load demands and the utilities consumed were documented. The results obtained shows good response when phone dialed, and the packet delivery of feedback SMS was quite satisfactory, making the implemented system to be of good quality with affordable cost and performs the desired functions. Besides, comparative analysis showed notable reduction in energy consumption and invariably lessened electrical bill of the consumer.

Keywords: automation, domotics, energy, load, remote, schedule

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Authors: Raana Babadi Fathipour

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Ice cream is a nutritious dairy product that, given its constituent materials and high nutritional value, is a suitable growth medium for the growth of various food microorganisms. The contamination of this product with pathogenic microorganisms may cause food poisoning and infections, and so could be harmful to human health. The foremost critical pathogenic microscopic organisms of ice cream incorporate Escherichia coli, Staphylococcus aureus, Bacillus cereus, Enterobacteriaceae, coliforms, Listeria monocytogenes and Enterococcus. Biosensor technology, albeit a recent addition to the dairy industry, has proven its worth in other fields, such as medical devices. Through numerous studies, the advantages of employing biosensors have consistently emerged. These incredible tools present expeditious and straightforward means while specifically targeting analytes. Thus, they bring forth unparalleled solutions that bolster ongoing advancements within dairy products and processes. This review delves into the latest developments in the realm of biosensors and evaluates the diverse techniques of bio-recognition and transduction in terms of their benefits, drawbacks, and relevance to traditional ice cream. Furthermore, the obstacles that impede the progress of these approaches in meeting the growing need for swift and real-time quality control of milk products, particularly ice cream, are also expounded upon.

Keywords: traditional ice cream, Escherichia coli, Staphylococcus aureus, biosensors

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Authors: Spandan Sahu, Amiya Kumar Pati, Kishanjit Kumar Khatua

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River is our main source of water which is a form of open channel flow and the flow in open channel provides with many complex phenomenon of sciences that needs to be tackled such as the critical flow conditions, boundary shear stress and depth averaged velocity. During floods, part of a river is carried by the simple main channel and rest is carried by flood plains. For such compound asymmetric channels, the flow structure becomes complicated due to momentum exchange between main channel and adjoining flood plains. Distribution of boundary shear in subsections provides us with the concept of momentum transfer between the interface of main channel and the flood plains. Experimentally, to get better data with accurate results are very complex because of the complexity of the problem. Hence, CES software has been used to tackle the complex processes to determine the shear stresses at different sections of an open channel having asymmetric flood plains on both sides of the main channel and the results is compared with the symmetric flood plains for various geometrical shapes and flow conditions. Error analysis is also performed to know the degree of accuracy of the model implemented.

Keywords: depth average velocity, non prismatic compound channel, relative flow depth, velocity distribution

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Authors: Tatsuya Iizuka, Takushi Kawamorita, Tomoya Handa, Hitoshi Ishikawa

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We report normative cone contrast sensitivity values and sensitivity and specificity values for a computer-based color vision test, the cone contrast test-HD (CCT-HD). The participants included 50 phakic eyes with normal color vision (NCV) and 20 dichromatic eyes (ten with protanopia and ten with deuteranopia). The CCT-HD was used to measure L, M, and S-CCT-HD scores (color vision deficiency, L-, M-cone logCS≦1.65, S-cone logCS≦0.425) to investigate the sensitivity and specificity of CCT-HD based on anomalous-type diagnosis with animalscope. The mean ± standard error L-, M-, S-cone logCS for protanopia were 0.90±0.04, 1.65±0.03, and 0.63±0.02, respectively; for deuteranopia 1.74±0.03, 1.31±0.03, and 0.61±0.06, respectively; and for age-matched NCV were 1.89±0.04, 1.84±0.04, and 0.60±0.03, respectively, with significant differences for each group except for S-CCT-HD (Bonferroni corrected α = 0.0167, p < 0.0167). The sensitivity and specificity of CCT-HD were 100% for protan and deutan in diagnosing abnormal types from 20 to 64 years of age, but the specificity decreased to 65% for protan and 55% for deutan in older persons > 65. CCT-HD is comparable to the diagnostic performance of the anomalous type in the anomaloscope for the 20-64-year-old age group. However, the results should be interpreted cautiously in those ≥ 65 years. They are more susceptible to acquired color vision deficiencies due to the yellowing of the crystalline lens and other factors.

Keywords: cone contrast test HD, color vision test, congenital color vision deficiency, red-green dichromacy, cone contrast sensitivity

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: M. Karthigayan, M. Rizon, Sazali Yaacob, R. Nagarajan, M. Muthukumaran, Thinaharan Ramachandran, Sargunam Thirugnanam

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In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate.

Keywords: ellipse fitness function, genetic algorithm, emotion recognition, fuzzy clustering

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Authors: Sophie N. Selby-Pham, Yudie Wang, Louise Bennett

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Consumption of grapes promotes health and reduces the risk of chronic diseases due to the action of grape phytochemicals in regulation of Oxidative Stress and Inflammation (OSI). The bioefficacy of phytochemicals depends on their absorption in the human body. The time required for phytochemicals to achieve maximal plasma concentration (Tₘₐₓ) after oral intake reflects the time window of maximal bioefficacy of phytochemicals, with Tₘₐₓ dependent on physicochemical properties of phytochemicals. This research collated physicochemical properties of grape phytochemicals from white and red grapes to predict their Tₘₐₓ using pharmacokinetic modelling. The predicted values of Tₘₐₓ were then compared to the measured Tₘₐₓ collected from clinical studies to determine the accuracy of prediction. In both liquid and solid intake forms, white grapes exhibit a shorter Tₘₐₓ range (0.5-2.5 h) versus red grapes (1.5-5h). The prediction accuracy of Tₘₐₓ for grape phytochemicals was 33.3% total error of prediction compared to the mean, indicating high prediction accuracy. Pharmacokinetic modelling allows prediction of Tₘₐₓ without costly clinical trials, informing dosing frequency for sustained presence of phytochemicals in the body to optimize the health benefits of phytochemicals.

Keywords: absorption kinetics, phytochemical, phytochemical absorption prediction model, Vitis vinifera

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Authors: Chothmal

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In this paper, we present a route discovery process which uses the signal strength on a link as a parameter of its inclusion in the route discovery method. The proposed signal-to-interference and noise ratio (SINR) based multipath reactive routing protocol is named as SINR-MP protocol. The proposed SINR-MP routing protocols has two following two features: a) SINR-MP protocol selects routes based on the SINR of the links during the route discovery process therefore it select the routes which has long lifetime and low frame error rate for data transmission, and b) SINR-MP protocols route discovery process is multipath which discovers more than one SINR based route between a given source destination pair. The multiple routes selected by our SINR-MP protocol are node-disjoint in nature which increases their robustness against link failures, as failure of one route will not affect the other route. The secondary route is very useful in situations where the primary route is broken because we can now use the secondary route without causing a new route discovery process. Due to this, the network overhead caused by a route discovery process is avoided. This increases the network performance greatly. The proposed SINR-MP routing protocol is implemented in the trail version of network simulator called Qualnet.

Keywords: ad hoc networks, quality of service, video streaming, H.264/SVC, multiple routes, video traces

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Authors: Qazi Sarah Rasheed

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Feminism and religion have been regarded as opposing binaries. The reason being that religion is regarded as a tool to legitimize the patriarchal control over women, and therefore, it stands in contrast with the basic feminist principle of gender equity. Hence, the issue of incompatibility between religion and gender parity is often discussed by the feminist as well as secular/liberal discourses, but the feminist discourse has suffered a serious backlash in the recent times for it alienates those women who want to liberate but not at the expense of their religious identity. Though in the Western feminist thought, religion is regarded as a tool of patriarchy that promotes women’s suppression, but for many women, religion can be a source of liberation that advances their rights. The feminists in general, fail to realize that religion, as a social phenomenon may not necessarily promote a series of dogmatic doctrines which are inevitably retrogressive or instinctively status-quoist especially when it comes to the social reforms affecting gender orders. The traditional institution of religion could be instrumental to provide what the women in contemporary situation demand. This paper highlights how the Muslim women in India negotiate and mediate this opposition in an Islamic context. To advance the socio-legal recognition of women’s rights, they question the male privilege and patriarchy in a meaningful way without challenging their Islamic doctrines and try to build a feminist consciousness from within religion.

Keywords: feminism, Islam, Muslim women's rights, religious identity

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Authors: Awdhesh Narayan Sharma

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The use of herbal medicines for the cure of various ailments among the tribal population is as old as human origin itself. Most of the tribal populations of Madhya Pradesh inhabit in remote and inaccessible ecological setup. From long back, tribals and forests are interrelated to each other. They use an enormous range of wild plants for their basic needs and medicines. The tribal developed a unique understanding with wild plants, herbs, etc., and earned specialized knowledge of disease pattern and curative therapy-through hard experiences, common sense, trial, and error methods. They have passed this knowledge through traditions, taboos, totems, folklore by words of mouth from generation to generation. Here, an attempt has been made to study the possible aspects of herbal medicine for the cure of Jaundice among the tribal populations of Madhya Pradesh, India, through primary data as well as available secondary data. The data have been collected from the 305 Bharias of Patalkot, Madhya Pradesh, India, and included available secondary source of data by various investigators. It may be concluded that a sizable herbal medicinal plants' wealth exists in Madhya Pradesh, India, which still awaits for scientific exploration. The existing herbal medicines used for the cure of jaundice need an extensive investigation from the pharmaceutical point of view.

Keywords: Bharias, herbal medicine, tribal, Madhya Pradesh

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Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

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Authors: M. Elham Ghabouli

Abstract:

Intermediate cities which also called medium size cities have an important role in the process of globalization. It is argued that, in some cases this type of cities may be depopulated or in otherwise may be transformed as the periphery of metropolitans so that the personal identity of the city and its local cultural heritage could suffer from its neighbor metropolitan. Over the last decades, the role of tourism in development process and the cultural heritage is increased. The effect of tourism in socio-economic growth makes motivation for study on tourism development in regional and urban planning process. Tourism potentially has a specific role in promoting sustainable development especially by its economic and socio-cultural effects. The positive role of tourism in local development and in cultural heritage should be empowered by urban and regional planning. Damavand is an intermediate city located in Tehran province, Iran. Considering its local specific characteristic like social structure, antiquities and natural monuments made a suitable case study for studying on urban tourism planning method. Focusing on recognition of historical and cultural heritage of Damavand, this paper tried to peruse cultural-historical heritage protecting issue through “base plan methodology” which is introduced as a first step of urban planning for intermediate cities.

Keywords: urban planning, tourism, cultural heritage, intermediate cities

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

Abstract:

Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Son Nguyen, Thanh Van, Ly Le

Abstract:

Ca2+ - dependent calmodulin kinase IIa (CaMKIIa) has recently been found to associate with protein tau missorting and polymerization in Alzheimer’s Disease (AD). However, there has yet inhibitors targeting CaMKIIa to investigate the correlation between CaMKIIa activity and protein tau polymer formation. Combining virtual screening and our statistics in binding contribution scoring function (BCSF), we rationally identified potential compounds that bind to specific CaMKIIa active site and specificity-affinity distribution of the ligand within the active site. Using molecular dynamics simulation, we identified structural stability of CaMKIIa and potent inhibitors, and site-directed bonding, separating non-specific and specific molecular interaction features. Despite of variation in confirmation of simulation time, interactions of the potent inhibitors were found to be strongly associated with the unique chemical features extracted from molecular binding poses. In addition, competitive inhibitors within CaMKIIa showed an important molecular recognition pattern toward specific ligand features. Our approach combining virtual screening with BCSF may provide an universally applicable method for precise identification in the discovery of compounds.

Keywords: Alzheimer’s disease, Ca 2+ -dependent calmodulin kinase IIa, protein tau, molecular docking

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Authors: Jamal R. Elbergali

Abstract:

Multiple Input Multiple Output (MIMO) systems are wireless systems with multiple antenna elements at both ends of the link. Wireless communication systems demand high data rate and spectral efficiency with increased reliability. MIMO systems have been popular techniques to achieve these goals because increased data rate is possible through spatial multiplexing scheme and diversity. Spatial Multiplexing (SM) is used to achieve higher possible throughput than diversity. In this paper, we propose a Zero-Forcing (ZF) detection using a combination of Ordered Successive Interference Cancellation (OSIC) and Zero Forcing using Interference Cancellation (ZF-IC). The proposed method used an OSIC based on Signal to Noise Ratio (SNR) ordering to get the estimation of last symbol (x ̃_(N_T )), then the estimated last symbol is considered to be an input to the ZF-IC. We analyze the Bit Error Rate (BER) performance of the proposed MIMO system over Rayleigh Fading Channel, using Binary Phase Shift Keying (BPSK) modulation scheme. The results show better performance than the previous methods.

Keywords: SNR, BER, BPSK, MIMO, modulation, zero forcing (ZF), OSIC, ZF-IC, spatial multiplexing (SM)

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Authors: Ayoub Alsarhan, Ahmed Otoom, Yousef Kilani, Abdel-Rahman al-GHuwairi

Abstract:

Wireless mesh networks (WMNs) have emerged recently to improve internet access and other networking services. WMNs provide network access to the clients and other networking functions such as routing, and packet forwarding. Spectrum scarcity is the main challenge that limits the performance of WMNs. Cognitive radio is proposed to solve spectrum scarcity problem. In this paper, we consider a cognitive wireless mesh network where unlicensed users (secondary users, SUs) can access free spectrum that is allocated to spectrum owners (primary users, PUs). Although considerable research has been conducted on spectrum allocation, spectrum assignment is still considered an important challenging problem. This problem can be solved using cognitive radio technology that allows SUs to intelligently locate free bands and access them without interfering with PUs. Our scheme considers several heuristics for spectrum allocation. These heuristics include: channel error rate, PUs activities, channel capacity and channel switching time. Performance evaluation of the proposed scheme shows that the scheme is able to allocate the unused spectrum for SUs efficiently.

Keywords: cognitive radio, dynamic spectrum access, spectrum management, spectrum sharing, wireless mesh networks

Procedia PDF Downloads 511