Search results for: bit error probability

Authors: Mary Ann Cristine R. Olgado

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Diversity in the classroom might make it difficult to promote individualized learning, but differentiated instruction that caters to students' various learning preferences may prove to be beneficial. Hence, this study examined the effectiveness of Hybrid-Differentiated Mathematics Instruction (HDMI) in improving the students’ academic performance in Mathematics. It employed the quasi-experimental research design by using a comparative analysis of the two variables: the experimental and control groups. The learning styles of the students were identified using the Grasha-Riechmann Student Learning Style Scale (GRSLSS), which served as the basis for designing differentiated action plans in Mathematics. In addition, adapted survey questionnaires, pre-tests, and post-tests were used to gather information and were analyzed using descriptive and correlational statistics to find the relationship between variables. The experimental group received differentiated instruction for a month, while the control group received traditional teaching instruction. The study found that Hybrid-Differentiated Mathematics Instruction (HDMI) improved the academic performance of Grade 11-TVL students, with the experimental group performing better than the control group. This program has effectively tailored the teaching methods to meet the diverse learning needs of the students, fostering and enhancing a deeper understanding of mathematical concepts in Statistics & Probability, both within and beyond the classroom.

Keywords: differentiated instruction, hybrid, learning styles, academic performance

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Authors: Merouane Salhi, Toufik Zebbiche

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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 doesn’t remain perfect. Its state equation change and it becomes for a real gas. In this case, the effects of molecular size and intermolecular 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 thermodynamical, physical and geometrical flow parameters, to find a general case for real gas. With the assumptions that Berthelot’s state equation accounts for the molecular size and intermolecular 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: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Rajesh Singh, Kailash Kale

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In this paper, the Binomial process type occurrence of software failures is considered and failure intensity has been characterized by one parameter Rayleigh class Software Reliability Growth Model (SRGM). The proposed SRGM is mathematical function of parameters namely; total number of failures i.e. η-0 and scale parameter i.e. η-1. It is assumed that very little or no information is available about both these parameters and then considering non-informative priors for both these parameters, the Bayes estimators for the parameters η-0 and η-1 have been obtained under square error loss function. The proposed Bayes estimators are compared with their corresponding maximum likelihood estimators on the basis of risk efficiencies obtained by Monte Carlo simulation technique. It is concluded that both the proposed Bayes estimators of total number of failures and scale parameter perform well for proper choice of execution time.

Keywords: binomial process, non-informative prior, maximum likelihood estimator (MLE), rayleigh class, software reliability growth model (SRGM)

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Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

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Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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Authors: Abdulrahman Alenezi

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This research paper investigates the surface heat transfer characteristics using computational fluid dynamics for orthogonal and inclined impinging jet. A jet Reynolds number (Rₑ) of 10,000, jet-to- plate spacing (H/D) of two and eight and two angles of impingement (α) of 45° and 90° (orthogonal) were employed in this study. An unconfined jet impinges steadily a constant temperature flat surface using air as working fluid. The numerical investigation is validated with an experimental study. This numerical study employs grid dependency investigation and four different types of turbulence models including the transition SSD to accurately predict the second local maximum in Nusselt number. A full analysis of the effect of both turbulence models and mesh size is reported. Numerical values showed excellent agreement with the experimental data for the case of orthogonal impingement. For the case of H/D =6 and α=45° a maximum percentage error of approximately 8.8% occurs of local Nusselt number at stagnation point. Experimental and numerical correlations are presented for four different cases

Keywords: turbulence model, inclined jet impingement, single jet impingement, heat transfer, stagnation point

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Authors: Kouzi Katia

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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

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Authors: S. Sonkham, U. Pinsopon, W. Chatlatanagulchai

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This paper presents a method of sliding mode control (SMC) designing and developing for the servo system in a dual-stage actuator (DSA) hard disk drive. Mathematical modelling of hard disk drive actuators is obtained, extracted from measuring frequency response of the voice-coil motor (VCM) and PZT micro-actuator separately. Matlab software tools are used for mathematical model estimation and also for controller design and simulation. A model-reference approach for tracking requirement is selected as a proposed technique. The simulation results show that performance of a model-reference SMC controller design in DSA servo control can be satisfied in the tracking error, as well as keeping the positioning of the head within the boundary of +/-5% of track width under the presence of internal and external disturbance. The overall results of model-reference SMC design in DSA are met per requirement specifications and significant reduction in %off track is found when compared to the single-state actuator (SSA).

Keywords: hard disk drive, dual-stage actuator, track following, hdd servo control, sliding mode control, model-reference, tracking control

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Authors: Al Omari Moahmmed Ahmed

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These papers describe the Bayesian Estimator using Markov Chain Monte Carlo and Lindley’s approximation and the maximum likelihood estimation of the Weibull distribution with Type-I censored data. The maximum likelihood method can’t estimate the shape parameter in closed forms, although it can be solved by numerical methods. Moreover, the Bayesian estimates of the parameters, the survival and hazard functions cannot be solved analytically. Hence Markov Chain Monte Carlo method and Lindley’s approximation are used, where the full conditional distribution for the parameters of Weibull distribution are obtained via Gibbs sampling and Metropolis-Hastings algorithm (HM) followed by estimate the survival and hazard functions. The methods are compared to Maximum Likelihood counterparts and the comparisons are made with respect to the Mean Square Error (MSE) and absolute bias to determine the better method in scale and shape parameters, the survival and hazard functions.

Keywords: weibull distribution, bayesian method, markov chain mote carlo, survival and hazard functions

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Authors: Khandoker Akib Mohammad

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While analyzing time-to-event data, it is possible that a certain fraction of subjects will never experience the event of interest, and they are said to be cured. When this feature of survival models is taken into account, the models are commonly referred to as cure models. In the presence of covariates, the conditional survival function of the population can be modelled by using the cure model, which depends on the probability of being uncured (incidence) and the conditional survival function of the uncured subjects (latency), and a combination of logistic regression and Cox proportional hazards (PH) regression is used to model the incidence and latency respectively. In this paper, we have shown the asymptotic normality of the profile likelihood estimator via asymptotic expansion of the profile likelihood and obtain the explicit form of the variance estimator with an implicit function in the profile likelihood. We have also shown the efficient score function based on projection theory and the profile likelihood score function are equal. Our contribution in this paper is that we have expressed the efficient information matrix as the variance of the profile likelihood score function. A simulation study suggests that the estimated standard errors from bootstrap samples (SMCURE package) and the profile likelihood score function (our approach) are providing similar and comparable results. The numerical result of our proposed method is also shown by using the melanoma data from SMCURE R-package, and we compare the results with the output obtained from the SMCURE package.

Keywords: Cox PH model, cure model, efficient score function, EM algorithm, implicit function, profile likelihood

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Authors: Angela U. Makolo, Temitayo A. Olagunju

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The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained using high throughput experimental procedures. However, these high throughput methods are known to produce very high rates of false positive and negative interactions. In order to construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed. A weighted interaction graph of the Saccharomyces cerevisiae (Baker’s Yeast) organism using the proteins as the nodes and interactions between them as edges was constructed. The weights were obtained using Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model. A pathway was formalized as a simple path in the interaction network from a starting protein and an ending protein of interest. We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. cerevisiae.

Keywords: Bayesian networks, protein interaction networks, Saccharomyces cerevisiae, signalling pathways

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Authors: Shih-Hao Chen, Chi-Wai Chow

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Multiple-input and multiple-output (MIMO) scheme can extend the transmission capacity for the light-emitting-diode (LED) visible light communication (VLC) system. The MIMO VLC system using the popular mobile-phone camera as the optical receiver (Rx) to receive MIMO signal from n x n Red-Green-Blue (RGB) LED array is desirable. The key step of decoding the received RGB LED array signals is detecting the direction of received array signals. If the LED transmitter (Tx) is rotated, the signal may not be received correctly and cause an error in the received signal. In this work, we propose and demonstrate a novel hierarchical transmission scheme which can reduce the computation complexity of rotation detection in LED array VLC system. We use the n x n RGB LED array as the MIMO Tx. A novel two dimension Hadamard coding scheme is proposed and demonstrated. The detection correction rate is above 95% in the indoor usage distance. Experimental results confirm the feasibility of the proposed scheme.

Keywords: Visible Light Communication (VLC), Multiple-input and multiple-output (MIMO), Red-Green-Blue (RGB), Hadamard coding scheme

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Authors: Meghana Shankara, Priyadarshini Natarajan

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Infrared (IR) spectroscopy has revolutionized the way we look at materials around us. Unraveling the pattern in the molecular spectra of materials to analyze the composition and properties of it has been one of the most interesting challenges in modern science. Applications of the IR spectrometry are numerous in the field’s pharmaceuticals, health, food and nutrition, oils, agriculture, construction, polymers, beverage, fabrics and much more limited only by the curiosity of the people. Near Infrared (NIR) spectrometry is applied robustly in analyzing the solids and liquid substances because of its non-destructive analysis method. In this paper, we have reviewed the application of NIR spectrometry in milk quality analysis and have presented the modes of measurement applied in NIRS measurement setup, Design of Experiment (DoE), classification/quantification algorithms used in the case of milk composition prediction like Fat%, Protein%, Lactose%, Solids Not Fat (SNF%) along with different approaches for adulterant identification. We have also discussed the important NIR ranges for the chosen milk parameters. The performance metrics used in the comparison of the various Chemometric approaches include Root Mean Square Error (RMSE), R^2, slope, offset, sensitivity, specificity and accuracy

Keywords: chemometrics, design of experiment, milk quality analysis, NIRS measurement modes

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: Hyun-Ho Lee, Kee-Won Kim

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The arithmetic operations over GF(2m) have been extensively used in error correcting codes and public-key cryptography schemes. Finite field arithmetic includes addition, multiplication, division and inversion operations. Addition is very simple and can be implemented with an extremely simple circuit. The other operations are much more complex. The multiplication is the most important for cryptosystems, such as the elliptic curve cryptosystem, since computing exponentiation, division, and computing multiplicative inverse can be performed by computing multiplication iteratively. In this paper, we present a parallel computation algorithm that operates Montgomery multiplication over finite field using redundant basis. Also, based on the multiplication algorithm, we present an efficient semi-systolic multiplier over finite field. The multiplier has less space and time complexities compared to related multipliers. As compared to the corresponding existing structures, the multiplier saves at least 5% area, 50% time, and 53% area-time (AT) complexity. Accordingly, it is well suited for VLSI implementation and can be easily applied as a basic component for computing complex operations over finite field, such as inversion and division operation.

Keywords: finite field, Montgomery multiplication, systolic array, cryptography

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Authors: Mohhamed El Habib Labdouni

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Introduction: Co-infection with VIH and tuberculosis poses one of the major ongoing challenges for global TB and AIDS prevention and control. The objective of this study is to raise the issue of the resurgence of TB, in People living with VIH supported in a referent center in western Algeria. Its epidemiological, clinical, biological and radiological new trends, and to compare the mortality rate between early and delayed ART. Methods: It was a prospective study, during 36 months from the 01st/01/2012 to 31st/12/2014, by identifying and analyzing cases of TB-VIH co-infection. Our population was devised in two groups/ early ART and delayed ART. The primary and secondary endpoints were analyzed with Kaplan-Meier curves and log-rank test the period of follow up, which was fixed at 300 weeks. Results: Sixty cases of co-infection TB -VIH were enrolled in our study: 78.3% had pulmonary tuberculosis associated with extra-pulmonary, 13.3% had only pulmonary tuberculosis and 08.3% presented strictly extra-pulmonary TB. The clinical particularity of this co-infection is the frequency of serious localization such us: pleural 23.3%, peritoneal 31.7%, and meningeal suffusion 13.3%.y-.biologicaly we notice the predominance both of pancytopenia and leucoanemia, hyponatremia in 38,6% and hypokalemia in 19,3%. By analyzing Kaplan-Meier survival curves, we notice that early ART initiation is associated with a significant reduction of all-cause mortality (p = 0,000), and we have identified several prognostic factors such as hypokalemia hyponatremia, leukocytosis thrombopenemia leucothrombopenia (p = 0,005). Conclusion: Our study confirms most of the results reported in the literature. Early ART initiation reduces the rate of all-cause mortality, despite the probability of the occurrence of TB-IRIS.

Keywords: TB-HIV co-infection, early ART, hyponatremia, extrapulmonary tuberculosis

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Adepoju A. A., Olapade-Ogunwole F., Ganiyu M. O.

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This study examined women's empowerment in cassava production in southwest Nigeria. The contributions of the five domains namely decision about agricultural production, decision-making power over productive resources, control of the use of income, leadership and time allocation to women disempowerment, profiled the women based on their socio-economics features and determined factors influencing women's disempowerment. Primary data were collected from the women farmers and processors through the use of structured questionnaires. Purposive sampling was used to select the LGAs and villages based on a large number of cassava farmers and processors, while cluster sampling was used to select 360 respondents in the study area. Descriptive statistics such as bar charts and percentages, Women Empowerment in Agriculture (WEAI), and the Logit regression model were used to analyze the data collected. The results revealed that 63.88% of the women were disempowered. Lack of decision-making power over productive resources; 36.47% and leadership skills; 33.26% contributed mostly to the disempowerment of the women. About 85% of the married women were disempowered, while 76.92% of the women who participated in social group activities were more empowered than their disempowered counterparts. The findings showed that women with more years of processing experience have the probability of being disempowered while those who engage in farming as a primary livelihood activity, and participate in social groups among others have the tendency to be empowered. In view of this, it was recommended that women should be encouraged to farm and contribute to social group activities.

Keywords: cassava, production, empowerment, southwest, Nigeria

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Authors: Abdelkader Elhanaoui, Mhamed Hadji, Rachid Skouri, Said Agounad

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In this paper, altera de1-SoC FPGA board technology is utilized as a distinguished tool for nondestructive characterization of an aluminum circular cylindrical shell of radius ratio b/a (a: outer radius; b: inner radius). The acoustic backscattered signal processing system has been developed using OpenMP architecture. The design is built in three blocks; it is implemented per functional block, in a heterogeneous Intel-Altera system running under Linux. The useful data to determine the performances of SoC FPGA is computed by the analytical method. The exploitation of SoC FPGA has lead to obtain the backscattering form function and resonance spectra. A0 and S0 modes of propagation in the tube are shown. The findings are then compared to those achieved from the Matlab simulation of analytical method. A good agreement has, therefore, been noted. Moreover, the detailed SoC FPGA-based system has shown that acoustic spectra are performed at up to 5 times faster than the Matlab implementation using almost the same data. This FPGA-based system implementation of processing algorithms is realized with a coefficient of correlation R and absolute error respectively about 0.962 and 5 10⁻⁵.

Keywords: OpenMP, signal processing system, acoustic backscattering, nondestructive characterization, thin tubes

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Authors: M. H. Korayem, N. Sangsefidi

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Magnetically guided soft robots have emerged as a promising technology in minimally invasive surgery due to their ability to adapt to complex environments. However, one of the main challenges in this field is damage to the vascular structure caused by unwanted stress on the vessel wall and deformation of the vessel due to improper control of the shape of the robot body during surgery. Therefore, this article proposes an approach for controlling the form of a magnetic, soft, continuous robot body using a PID controller. The magnetic soft continuous robot is modelled using Cosserat theory in static mode and solved numerically. The designed controller adjusts the position of each part of the robot to match the desired shape. The PID controller is considered to minimize the robot's contact with the vessel wall and prevent unwanted vessel deformation. The simulation results confirmed the accuracy of the numerical solution of the static Cosserat model. Also, they showed the effectiveness of the proposed contouring method in achieving the desired shape with a maximum error of about 0.3 millimetres.

Keywords: PID, magnetic soft continuous robot, soft robot shape control, Cosserat theory, minimally invasive surgery

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Authors: N. H. Adenan, M. S. M. Noorani

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River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources.

Keywords: river flow, nonlinear prediction method, phase space, local linear approximation

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Authors: Prayas Sharma

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This paper proposed a generalized class of estimators, an exponential class of estimators based on the adaption of Sharma and Singh (2015) and Solanki and Singh (2013), and a simple difference estimator for estimating unknown population mean in the case of Poisson distributed population in simple random sampling without replacement. The expressions for mean square errors of the proposed classes of estimators are derived from the first order of approximation. It is shown that the adapted version of Solanki and Singh (2013), the exponential class of estimator, is always more efficient than the usual estimator, ratio, product, exponential ratio, and exponential product type estimators and equally efficient to simple difference estimator. Moreover, the adapted version of Sharma and Singh's (2015) estimator is always more efficient than all the estimators available in the literature. In addition, theoretical findings are supported by an empirical study to show the superiority of the constructed estimators over others with an application to earthquake data of Turkey.

Keywords: auxiliary attribute, point bi-serial, mean square error, simple random sampling, Poisson distribution

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Authors: María Isabel Núñez-Peña, Macarena Suárez Pellicioni

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In the present study, we used event-related potentials (ERP) to address the question of whether high (HMA) and low math-anxious (LMA) individuals differ on a complex addition verification task, which involved both carrying and non-carrying additions. ERPs were recorded while seventeen HMA and seventeen LMA individuals performed the verification task. Groups did not differ in trait anxiety or gender distribution. Participants were presented with two-digit additions and were asked to decide whether the proposed solution was correct or incorrect. Behavioral data showed a significant Carrying x Proposed solution x Group interaction for accuracy, showing that carrying additions were more error prone than non-carrying ones for both groups, although the difference non-carrying minus carrying was larger for the HMA group. As for ERPs, a P2 component larger in HMA individuals than in their LMA peers was found both for carrying and non-carrying additions. The P2 was followed by a sustained negative slow wave at parietal positions. Because the negative slow waves are thought to reflect the updating of working memory, these results give support to the relationship among working memory, math performance and math anxiety.

Keywords: math anxiety, carrying, working memory, P2

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Authors: Maha Saliba Foster

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Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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Authors: Erin C. Rodenburg, Jennifer McWhirter, Andrew Papadopoulos

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Support workers for adults with developmental disabilities promote the care and wellbeing of a historically underserved population. Poor employment training and low work satisfaction for these disability support workers are linked to low productivity, poor quality of care, turnover, and intention to leave employment. Therefore, to improve the lives of those within disability support homes, both client and caregiver, it is vital to determine where improvements to training and support for those providing direct care can be made. The current study aims to explore disability support worker’s perceptions of the training received in their employment at the residential homes, how it prepared them for their role, and where there is room for improvement with the aim of developing recommendations for an improved training experience. Responses were collected from 85 disability support workers across 40 Ontario group homes. Findings suggest most disability support workers within the 40 support homes feel adequately trained in their responsibilities of employment. For those who did not feel adequately trained, the main issues expressed were a lack of standardization in training, a need for more continuous training, and a move away from trial and error in performing tasks to support clients with developmental disabilities.

Keywords: developmental disabilities, disability workers, support homes, training

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Authors: Israa Al-Neami, Ali J. Al-Askery, Martin Johnston, Charalampos Tsimenidis

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In this paper, we focus on the design of a multi-line copper wire (MLCW) communication system. First, we construct our proposed MLCW channel and verify its characteristics based on the Kolmogorov-Smirnov test. In addition, we apply Middleton class A impulsive noise (IN) to the copper channel for further investigation. Second, the MIMO G.fast system is adopted utilizing the proposed MLCW channel model and is compared to a single line G-fast system. Second, the performance of the coded system is obtained utilizing concatenated interleaved Reed-Solomon (RS) code with four-dimensional trellis-coded modulation (4D TCM), and compared to the single line G-fast system. Simulations are obtained for high quadrature amplitude modulation (QAM) constellations that are commonly used with G-fast communications, the results demonstrate that the bit error rate (BER) performance of the coded MLCW system shows an improvement compared to the single line G-fast systems.

Keywords: G.fast, Middleton Class A impulsive noise, mitigation techniques, Copper channel model

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Authors: Norsyamimi Hassim, Masturah Markom

Abstract:

Supercritical fluid extraction (SFE) has been known as a sustainable and safe extraction technique for plant extraction due to the minimal usage of organic solvent. In this study, a scale-up process for the selected herbal plant (Phyllanthus niruri) was investigated by using supercritical carbon dioxide (SC-CO2) with food-grade (ethanol-water) cosolvent. The quantification of excess ethanol content in the final dry extracts was conducted to determine the safety of enriched extracts. The extraction yields obtained by scale-up SFE unit were not much different compared to the predicted extraction yields with an error of 2.92%. For component contents, the scale-up extracts showed comparable quality with laboratory-scale experiments. The final dry extract showed that the excess ethanol content was 1.56% g/g extract. The fish embryo toxicity test (FETT) on the zebrafish embryos showed no toxicity effects by the extract, where the LD50 value was found to be 505.71 µg/mL. Thus, it has been proven that SFE with food-grade cosolvent is a safe extraction technique for the production of bioactive compounds from P. niruri.

Keywords: scale-up, supercritical fluid extraction, enriched extract, toxicity, ethanol content

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Authors: T. J. Ellapen, N. Acampora, S. Dawson, J. Arling, C. Van Niekerk, H. J. Van Heerden

Abstract:

Objectives: To determine the comparative proprioceptive performance of injured versus non-injured adolescent female hockey players when wearing an ankle brace. Methods: Data were collected from 100 high school players who belonged to the Highway Secondary School KZN Hockey league via voluntary parental informed consent and player assent. Players completed an injury questionnaire probing the prevalence and nature of hockey injuries (March-August 2013). Subsequently players completed a Biodex proprioceptive test with and without an ankle brace. Probability was set at p≤ 0.05. Results: Twenty-two players sustained ankle injuries within the six months (p<0.001). Injured players performed similarly without bracing Right Anterior Posterior Index (RAPI): 2.8±0.9; Right Medial Lateral Index (RMLI): 1.9±0.7; Left Anterior Posterior Index (LAPI) LAPI: 2.7; Left Medial Lateral Index (LMLI): 1.7±0.6) as compared to bracing (RAPI: 2.7±1.4; RMLI: 1.8±0.6; LAPI: 2.6±1.0; LMLI: 1.5±0.6) (p>0.05). However, bracing (RAPI: 2.2±0.8; RMLI: 1.5±0.5; LAPI: 2.4±0.9; MLI: 1.5±0.5) improved the ankle stability of the non-injured group as compared to their unbraced performance (RAPI: 2.5±1.0; RMLI: 1.8±0.8; LAPI: 2.8±1.1; LMLI: 1.8±0.6) (p<0.05). Conclusion: Ankle bracing did not enhance the stability of injured ankles. However ankle bracing has an ergogenic effect enhancing the stability of healthy ankles.

Keywords: hockey, proprioception, ankle, bracing

Procedia PDF Downloads 329