Archives-December 

Single-crystalline magnesium borate Mg2B2O5 nanowires in bundle form have been synthesized via a simple route based on heating a precursor powder made of aqueous solutions of magnesium chloride and de-sodium tetraborate with citric acid. The results show that each bundle composed of nanowires of high-purity with diameter of approximately ca. 90 nm and lengths up to a few micrometers. The effect of citric acid, the optimum experimental parameters and possible growth mechanism for the compound nanowires have been presented.

Hydrological modelling such as rainfall-runoff modeling and climate modelling is one of the most important and challenging task in the modern world. In general, climate, rainfall and runoff are highly non-linear and complicated phenomena, which require advanced computer modeling and simulation for their accurate prediction. An Artificial Neural Network (ANN) can be used to predict the behavior of such nonlinear systems. ANN has been successfully used by most of the researchers in this field for the last twenty-five years. This paper provides a survey of available literature of some methodologies employed by different researchers to utilize ANN for rainfall-runoff and climate change prediction. The survey also reports that such hydrological modelling using ANN technique is more suitable than traditional statistical and numerical methods.

This paper presents a new approach to the Automatic detection and classification of electrocardiogram (ECG) signals is of huge importance for diagnosis of cardiac abnormalities. A method is proposed here to classify different cardiac abnormalities like Ventricular Arrythmias, Myocardial infarction,Myocardial hypertrophy and Valvular heart disease. Support Vector Machine (SVM) has been used to classify the patterns inherent in the features extracted through Continuous Wavelet Transform (CWT) of different ECG signals. CWT allows a time domain signal to be transformed into time-frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of feature from non-stationary signals like ECG. Then the support vector machine (SVM) with Gaussian kernel is used to classify different ECG heart rhythm. In the present work, SVM in regression mode has been successfully applied for the classification of cardiac abnormalities with good diagnostic accuracy.

The study assessed the effectiveness of support structures available to orphans in rural communities of Ibadan South East Local government area of Oyo state, Nigeria. Multi-stage sampling technique comprising of probability and non-probability sampling methods was used to select one hundred and twenty orphans from 10 rural communities of the local government. The main findings of the study show that the mean age of the orphans is 16.6 years with close to two-third (63.33%) being females. Seventy five percent had no formal education while 90% were not schooling; they receive a mean income of less than N80 per day. The respondents as one of the adjustment strategies are engaged in one agricultural economic activities or the other with higher proportion (67.50%) into food processing. About 16.67% do not engage in any non-agricultural activity while majority (38.33%) of them is into trading. Most of the orphans have been exposed to abuses such as maltreatment, rape, discrimination and street hawking with 81.67% of them exposed to more than one of the abuses. The feeling of inferiority complex is the most common condition under which the orphans live. Food, clothing and education were the only support structures available to orphans that are effective as revealed by the study. Due to the ineffectiveness of the support structures in the study area, all orphans involved in this study were found to be involved in at least one agricultural activity, most especially, in food processing.

Poor strategy execution has been blamed for strategy failure in many organizations. Strategy maps, a tool developed by Kaplan and Norton of the Balanced Scorecard Collaboration, have been hailed in both theory and practice as a robust tool in aligning strategy execution. This paper investigates strategy deployment at a firm that has not yet embraced strategy maps with the intention to demonstrate from a theoretical point of view the potential benefits of deploying the company’s strategy using strategy maps. This is achieved through a thorough study of the company’s current performance measurement system. The study identifies the operational weaknesses inherent in the current performance measurement system. In the context of the identified weaknesses, the paper projects how strategy maps can lever and align strategy execution in the attainment of the company’s mission. Keywords: Align; Balanced scorecard; Money transfer agency; Strategy execution; Strategy maps

English language anxiety and interest are considered as two important affective variables which are highly correlated to foreign language learning. The aim of this study was to explore the relationship between the English language anxiety, English language interest and English language achievement. The sample size comprised of 97 undergraduate students of BS Computer Sciences, Telecom engineering and Computer engineering program. The Pearson correlation analysis and multiple regressions are used to analyze the data. The results revealed that English language anxiety has significant negative correlation with English language interest and English language achievement. It is also found that English language interest and English language achievement has significant positive correlation. It has been observed that mostly males have less English language anxiety and more English language interest as compared to females. The rate of anxiety in females has significant negative relationship with achievement. The study reveals that the performance of the students of in English language is influenced by the English language anxiety.

Land use and land cover change play a pivotal role in global environmental change. They contribute significantly to earth-atmosphere interactions and biodiversity loss, are major factors in sustainable development and human responses to global change, and are important for integrated modeling and assessment of environmental issues in general The land cover map and land use map can be produced by field research and observation and interpretation of the large scale aerial pictures, but both of them are time and cost consuming. The main advantage of satellite images is that the classification is able to be repeatedly performed by simultaneous usage of multiple images during a short time. Applying the satellite data is a proper way in order to producing the land cover map and monitoring it especially in the vast geographical regions. The iterative self organizing data analysis technique (isodata) method used a set of rule-of-thumb procedures. Many of the steps used in the algorithm are based on the experience obtained through experimentation. According to evaluate signature file the optimal number of classes is 11.after determining of best classified NDVI map processed of spot NDVI maps for a new set of the hyper temporal. Drawinggraphs of mean digital number help to us for determined kind of classes. According to the graph of the spectral behavior of each class and fieldwork were determined land covers types. The optimum numbers of classes are 11 classes in the case study region based on the divergence of a minimum of separability. Spectral behavior shows the highest mean digital number in 11th class that starts in the spring season and finish in winter. First to fifth class has spectral behavior to each other. Mean of digital number of different years not same each other years and have different actions.

Multiview video coding is an extension of H264/AVC. When an Multiview video coding bitstream is decoded, some views (named target views) are to be is played; some other views (named dependent views) may not be displayed but are needed for inter-view prediction of the target views. The original Multiview video coding design requires pictures of the dependent views to be fully decoded and stored. This entails both high decoding complexity and high memory consumption for the pictures in the views which are not intended for display, particularly when the number of dependent views is large. In this paper, a single motion compensation loop decoding scheme is introduced to address these disadvantages. The proposed scheme requires only partial decoding of pictures in dependent views and thus significantly reduces decoding complexity and memory consumption. The proposed method is based on the motion skip, wherein inter-view motion and coding mode prediction is exploited. Simulation results shows that the proposed scheme provides a substantial reduction of complexity and memory size, at the expense of only a minor compression efficiency loss, compared with multiple motion compensation loop decoding schemes for Multiview video coding.

This paper deals with the simulation of three, five, seven and nine level output using H- bridge inverter. This paper presents H – bridge inverter simulated using MATLAB with different levels (like three, five, seven and nine level). The percentage (%) total harmonic distortion THD is calculated. The harmonic reduction is achieved by selecting appropriate switching angles. The functionality verification of the three level, five level, seven level and nine level output is done using MATLAB.

This piece of research addresses an interdisciplinary challenging issue concerned with dynamical evaluation of e-Learning systems' performance. More precisely, it presents an interdisciplinary work integrating neuronal, psychology, cognitive, and computer sciences into educational environment. That's in order to introduce systematic analysis and dynamical evaluation of the adopted study for e-learners' time response (equivalently convergence time) phenomenon. Specifically, this work concentrates on dynamical evaluation of one measuring parameter fore-learning performance namely: time response. In other words, e-learner's response time has been adopted as an appropriate candidate learning parameter applicable for reaching optimal analysis and evaluation of e-learning systems performance. Herein, that time considered as period of time requested in order to reach correctly a pre-assigned (desired) output answer which determined by an e-learner while examined via Multiple Choice Questions (MCQ). At the macro-level, the paper proposed e-learner's response time affected mostly by two basic extrinsic and intrinsic educational factors. Firstly, that associated to effectiveness of e-learning environment such as communication signal to noise ratio, and learning rate value. Secondly, that tightly coupled with gain factor candidates' brain function and structure (synapses, axons, and dendrites).Such as the number of dynamically contributing neurons, and the gain factor of neuronal response function. Consequently, Artificial Neural Networks (ANNs) simulation has been adopted for realistic evaluation of timely dependent candidate's response till reaching desired correct output solution for any arbitrary MCQ exam. After successful timely updating of dynamical state pattern (synaptic weight vector), pre-assigned (desired) correct response is accomplished in accordance with coincidence learning modeling. The presented simulation has been developed towards quantified analysis of the highly specialized neurons' role performed to select correct answers to MCQ. Furthermore, the time response parameter considers individual differences of learners' brain role (considering various number of neurons), while performing selectivity (MCQ) processes. Finally, after running of suggested realistic simulation programs, some interesting conclusive results introduced.