Search results for: Square Enclosure

Authors: Umm-e- Kalsoom, M. Arshad, Sadia Iqbal, M. Usman, M. Adnan

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The research study evaluated the performance of irrigation system by using special scientific tools like Remote Sensing and GIS technology, so that proper measurements could be taken for the sustainable agriculture and water management. Different performance evaluation parameters had been calculated for the purposed data was gathered from field investigation and different government and private organizations. According to the calculations, organic matter ranges from 0.19% (low value) to 0.76% (high value). In flat irrigation system for wheat yield ranges from 3347.16 to 5260.39 kg/ha, while the total water applied to wheat crop ranges from 252.94 to 279.19 mm and WUE ranges from 13.07 to 18.37 kg/ha/mm. For rice yield ranges from 3347.47 to 5433.07 kg/ha with total water supplied to rice crop ranges from 764.71 to 978.15 mm and WUE ranges from 3.49 to 5.71 kg/ha/mm. Similarly, in raised bed system wheat yield ranges from 4569.13 to 6008.60 kg/ha, total water supplied ranges from 158.87 to 185.09 mm and WUE ranges from 27.20 to 33.54 kg/ha/mm while in rice crop, yield ranges from 5285.04 to 6716.69 kg/ha, total water supplied ranges from 600.72 to 755.06 mm and WUE ranges from 6.41 to 10.05 kg/ha/mm. Almost 51.3% water saving is observed in bed irrigation system as compared to flat system. Less water supplied to beds is more affective as its WUE value is higher than flat system where more water is supplied in both the seasons. Similarly, RWS values show that maximum water deficit while minimum area is getting adequate water supply. Greater yield is recorded in bed system as plant per square meter is more in bed system in comparison of flat system Thus, the integration of GIS tools to regularly compute performance indices could provide irrigation managers with the means for managing efficiently the irrigation system.

Keywords: Field survey, Relative Water Supply (RWS), Remote sensing maps, Water Use Efficiency (WUE).

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Authors: Migbar Assefa

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The performance of a machine tool is eventually assessed by its ability to produce a component of the required geometry in minimum time and at small operating cost. It is customary to base the structural design of any machine tool primarily upon the requirements of static rigidity and minimum natural frequency of vibration. The operating properties of machines like cutting speed, feed and depth of cut as well as the size of the work piece also have to be kept in mind by a machine tool structural designer. This paper presents a novel approach to the design of machine tool column for static and dynamic rigidity requirement. Model evaluation is done effectively through use of General Finite Element Analysis software ANSYS. Studies on machine tool column are used to illustrate finite element based concept evaluation technique. This paper also presents results obtained from the computations of thin walled box type columns that are subjected to torsional and bending loads in case of static analysis and also results from modal analysis. The columns analyzed are square and rectangle based tapered open column, column with cover plate, horizontal partitions and with apertures. For the analysis purpose a total of 70 columns were analyzed for bending, torsional and modal analysis. In this study it is observed that the orientation and aspect ratio of apertures have no significant effect on the static and dynamic rigidity of the machine tool structure.

Keywords: Finite Element Modeling, Modal Analysis, Machine tool structure, Static Analysis.

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Authors: V. Pizarro-Carmona, S. Castano-Solis, M. Cortés-Carmona, J. Fraile-Ardanuy, D. Jimenez-Bermejo

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The purpose of this article is to optimize the Equivalent Electric Circuit Model (EECM) of different orders to obtain greater precision in the modeling of Li-ion battery packs. Optimization includes considering circuits based on 1RC, 2RC and 3RC networks, with a dependent voltage source and a series resistor. The parameters are obtained experimentally using tests in the time domain and in the frequency domain. Due to the high non-linearity of the behavior of the battery pack, Genetic Algorithm (GA) was used to solve and optimize the parameters of each EECM considered (1RC, 2RC and 3RC). The objective of the estimation is to minimize the mean square error between the measured impedance in the real battery pack and those generated by the simulation of different proposed circuit models. The results have been verified by comparing the Nyquist graphs of the estimation of the complex impedance of the pack. As a result of the optimization, the 2RC and 3RC circuit alternatives are considered as viable to represent the battery behavior. These battery pack models are experimentally validated using a hardware-in-the-loop (HIL) simulation platform that reproduces the well-known New York City cycle (NYCC) and Federal Test Procedure (FTP) driving cycles for electric vehicles. The results show that using GA optimization allows obtaining EECs with 2RC or 3RC networks, with high precision to represent the dynamic behavior of a battery pack in vehicular applications.

Keywords: Li-ion battery packs modeling optimized, EECM, GA, electric vehicle applications.

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: Data fusion, Gaussian process regression, signal denoise, temporal extrapolation.

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Authors: Zahra Ghadampour, Gholamreza Rakhshandehroo

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A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.

Keywords: Neural networks, groundwater depth, forecast.

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Authors: Gholamhossein Hosseini

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Two earliness cotton genotypes I and II, which had been developed by hybridization and backcross methods between sindise-80 as an early maturing gene parent and two other lines i.e. Red leaf and Bulgare-557 as a second parent, are subjected to different environmental conditions. The early maturing genotypes with coded names of I and II were compared with four native cotton cultivars in randomized complete block design (RCBD) with four replications in three ecological regions of Iran from 2016-2017. Two early maturing genotypes along with four native cultivars viz. Varamin, Oltan, Sahel and Arya were planted in Agricultural Research Station of Varamin, Moghan and Kashmar for evaluation. Earliness data were collected for six treatments during two years in the three regions except missing data for the second year of Kashmar. Therefore, missed data were estimated and imputed. For testing the homogeneity of error variances, each experiment at a given location or year is analyzed separately using Hartley and Bartlett’s Chi-square tests and both tests confirmed homogeneity of variance. Combined analysis of variance showed that genotypes I and II were superior in Varamin, Moghan and Kashmar regions. Earliness means and their interaction effects were compared with Duncan’s multiple range tests. Finally combined analysis of variance showed that genotypes I and II were superior in Varamin, Moghan and Kashmar regions. Earliness means and their interaction effects are compared with Duncan’s multiple range tests.

Keywords: Cotton, combined, analysis, earliness.

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Authors: Sunitha. S.L., V. Udayashankara

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Background noise is particularly damaging to speech intelligibility for people with hearing loss especially for sensorineural loss patients. Several investigations on speech intelligibility have demonstrated sensorineural loss patients need 5-15 dB higher SNR than the normal hearing subjects. This paper describes Discrete Cosine Transform Power Normalized Least Mean Square algorithm to improve the SNR and to reduce the convergence rate of the LMS for Sensory neural loss patients. Since it requires only real arithmetic, it establishes the faster convergence rate as compare to time domain LMS and also this transformation improves the eigenvalue distribution of the input autocorrelation matrix of the LMS filter. The DCT has good ortho-normal, separable, and energy compaction property. Although the DCT does not separate frequencies, it is a powerful signal decorrelator. It is a real valued function and thus can be effectively used in real-time operation. The advantages of DCT-LMS as compared to standard LMS algorithm are shown via SNR and eigenvalue ratio computations. . Exploiting the symmetry of the basis functions, the DCT transform matrix [AN] can be factored into a series of ±1 butterflies and rotation angles. This factorization results in one of the fastest DCT implementation. There are different ways to obtain factorizations. This work uses the fast factored DCT algorithm developed by Chen and company. The computer simulations results show superior convergence characteristics of the proposed algorithm by improving the SNR at least 10 dB for input SNR less than and equal to 0 dB, faster convergence speed and better time and frequency characteristics.

Keywords: Hearing Impairment, DCT Adaptive filter, Sensorineural loss patients, Convergence rate.

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Authors: M. S. Ahmed, F. A. Mohammad

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This paper focuses on the dynamic behavior of reinforced concrete (RC) slabs. Therefore, the theoretical modal analysis was performed using two different types of boundary conditions. Modal analysis method is the most important dynamic analyses. The analysis would be modal case when there is no external force on the structure. By using this method in this paper, the effects of freely and simply supported boundary conditions on the frequencies and mode shapes of RC square slabs are studied. ANSYS software was employed to derive the finite element model to determine the natural frequencies and mode shapes of the slabs. Then, the obtained results through numerical analysis (finite element analysis) would be compared with the exact solution. The main goal of the research study is to predict how the boundary conditions change the behavior of the slab structures prior to performing experimental modal analysis. Based on the results, it is concluded that simply support boundary condition has obvious influence to increase the natural frequencies and change the shape of the mode when it is compared with freely supported boundary condition of slabs. This means that such support conditions have the direct influence on the dynamic behavior of the slabs. Thus, it is suggested to use free-free boundary condition in experimental modal analysis to precisely reflect the properties of the structure. By using free-free boundary conditions, the influence of poorly defined supports is interrupted.

Keywords: Natural frequencies, Mode shapes, Modal analysis, ANSYS software, RC slabs.

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju

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The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.

Keywords: Comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events.

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Authors: Mojisola F. Oyewole, Franscisca T. Adetoro, Nkiru T. Meludu

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Moringa oleifera is a nutritious vegetable tree with varieties of potential uses, as almost every part of the Moringa oleifera tree can be used for food. This study was conducted in Oyo State, Nigeria, to find out the level of acceptability of Moringa oleifera diversified products among rural and urban dwellers. Purposive sampling was used to select two local governments’ areas. Stratified sampling technique was also used to select one community each from rural and urban areas while snowball sampling technique was used to select ten respondents each from the two communities, making a total number of forty respondents. Data were analyzed using frequencies, percentages, Chi-square, Pearson Product Moment Correlation and regression analysis. Result from the study revealed that majority of the respondents (80%) fell within the age range of 20-49 years and 55% of them were male, 55% were married, 70% of them were Christians, 80% of them had tertiary education. The result also showed that 85% were aware of the Moringa plant and (65%) of them have consumed Moringa oleifera and the perception statements on the benefits of Moringa oleifera indicated that (52.5%) of the respondents rated Moringa oleifera to be favorable, most of them had high acceptability for Moringa egusi soup, Moringa tea, Moringa pap and yam pottage with Moringa. The result of the hypotheses testing showed that there is a significant relationship between sex of the respondents and acceptability of the diversified Moringa oleifera products (x2=6.465, p = 0.011). There is also a significant relationship between family size of the respondents level of acceptability of the Moringa oleifera products (r = 0.327, p = 0.040). Based on the level of acceptability of Moringa oleifera diversified products; the plant is of great economic importance to the populace. Therefore, there should be more public awareness through the media to enlighten people on the beneficial effects of Moringa oleifera.

Keywords: Acceptability, Moringa oleifera, Diversified, Product, Dwellers.

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Authors: M. Yilmaz, I. Yilmaz, M. Uysal

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The Earth system generates different phenomena that are observable at the surface of the Earth such as mass deformations and displacements leading to plate tectonics, earthquakes, and volcanism. The dynamic processes associated with the interior, surface, and atmosphere of the Earth affect the three pillars of geodesy: shape of the Earth, its gravity field, and its rotation. Geodesy establishes a characteristic structure in order to define, monitor, and predict of the whole Earth system. The traditional and new instruments, observables, and techniques in geodesy are related to the gravity field. Therefore, the geodesy monitors the gravity field and its temporal variability in order to transform the geodetic observations made on the physical surface of the Earth into the geometrical surface in which positions are mathematically defined. In this paper, the main components of the gravity field modeling, (Free-air and Bouguer) gravity anomalies are calculated via recent global models (EGM2008, EIGEN6C4, and GECO) over a selected study area. The model-based gravity anomalies are compared with the corresponding terrestrial gravity data in terms of standard deviation (SD) and root mean square error (RMSE) for determining the best fit global model in the study area at a regional scale in Turkey. The least SD (13.63 mGal) and RMSE (15.71 mGal) were obtained by EGM2008 for the Free-air gravity anomaly residuals. For the Bouguer gravity anomaly residuals, EIGEN6C4 provides the least SD (8.05 mGal) and RMSE (8.12 mGal). The results indicated that EIGEN6C4 can be a useful tool for modeling the gravity field of the Earth over the study area.

Keywords: Free-air gravity anomaly, Bouguer gravity anomaly, global model, land gravity.

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Authors: Mliki Bouchmel, Belgacem Nabil, Abbassi Mohamed Ammar, Geudri Kamel, Omri Ahmed

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In this numerical work, mixed convection and entropy generation of Cu–water nanofluid in a lid-driven square cavity have been investigated numerically using the Lattice Boltzmann Method. Horizontal walls of the cavity are adiabatic and vertical walls have constant temperature but different values. The top wall has been considered as moving from left to right at a constant speed, U0. The effects of different parameters such as nanoparticle volume concentration (0–0.05), Rayleigh number (104–106) and Reynolds numbers (1, 10 and 100) on the entropy generation, flow and temperature fields are studied. The results have shown that addition of nanoparticles to the base fluid affects the entropy generation, flow pattern and thermal behavior especially at higher Rayleigh and low Reynolds numbers. For pure fluid as well as nanofluid, the increase of Reynolds number increases the average Nusselt number and the total entropy generation, linearly. The maximum entropy generation occurs in nanofluid at low Rayleigh number and at high Reynolds number. The minimum entropy generation occurs in pure fluid at low Rayleigh and Reynolds numbers. Also at higher Reynolds number, the effect of Cu nanoparticles on enhancement of heat transfer was decreased because the effect of lid-driven cavity was increased. The present results are validated by favorable comparisons with previously published results. The results of the problem are presented in graphical and tabular forms and discussed.

Keywords: Entropy generation, mixed convection, nanofluid, lattice Boltzmann method.

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Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

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Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: Airborne laser scanning, digital terrain models, filtering, forested areas.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: Deep learning, artificial neural networks, energy price forecasting, Turkey.

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: Energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning.

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Authors: Won Yeol Choi, Sangmo Kang

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The propulsion of a bacterial flagellum in a viscous fluid has attracted many interests in the field of biological hydrodynamics, but remains yet fully understood and thus still a challenging problem. In this study, therefore, we have numerically investigated the flow around a steadily rotating micro-sized spring to further understand such bacterial flagellum propulsion. Note that a bacterium gains thrust (propulsive force) by rotating the flagellum connected to the body through a bio motor to move forward. For the investigation, we convert the spring model from the micro scale to the macro scale using a similitude law (scale law) and perform simulations on the converted macro-scale model using a commercial software package, CFX v13 (ANSYS). To scrutinize the propulsion characteristics of the flagellum through the simulations, we make parameter studies by changing some flow parameters, such as the pitch, helical radius and rotational speed of the spring and the Reynolds number (or fluid viscosity), expected to affect the thrust force experienced by the rotating spring. Results show that the propulsion characteristics depend strongly on the parameters mentioned above. It is observed that the forward thrust increases in a linear fashion with either of the rotational speed or the fluid viscosity. In addition, the thrust is directly proportional to square of the helical radius and but the thrust force is increased and then decreased based on the peak value to the pitch. Finally, we also present the appropriate flow and pressure fields visualized to support the observations.

Keywords: Fluid viscosity, hydrodynamics, similitude, propulsive force.

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based transformer models, which had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the website of University of California, Irvine (UCI), which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean   Absolute Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: Air quality prediction, deep learning algorithms, time series forecasting, look-back window.

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Authors: Mamta Garg

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While compressing text files is useful, compressing still image files is almost a necessity. A typical image takes up much more storage than a typical text message and without compression images would be extremely clumsy to store and distribute. The amount of information required to store pictures on modern computers is quite large in relation to the amount of bandwidth commonly available to transmit them over the Internet and applications. Image compression addresses the problem of reducing the amount of data required to represent a digital image. Performance of any image compression method can be evaluated by measuring the root-mean-square-error & peak signal to noise ratio. The method of image compression that will be analyzed in this paper is based on the lossy JPEG image compression technique, the most popular compression technique for color images. JPEG compression is able to greatly reduce file size with minimal image degradation by throwing away the least “important" information. In JPEG, both color components are downsampled simultaneously, but in this paper we will compare the results when the compression is done by downsampling the single chroma part. In this paper we will demonstrate more compression ratio is achieved when the chrominance blue is downsampled as compared to downsampling the chrominance red in JPEG compression. But the peak signal to noise ratio is more when the chrominance red is downsampled as compared to downsampling the chrominance blue in JPEG compression. In particular we will use the hats.jpg as a demonstration of JPEG compression using low pass filter and demonstrate that the image is compressed with barely any visual differences with both methods.

Keywords: JPEG, Discrete Cosine Transform, Quantization, Color Space Conversion, Image Compression, Peak Signal to Noise Ratio & Compression Ratio.

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Authors: Pedro Pablo González Pérez, Hiram I. Beltrán, Arturo Rojo-Domínguez, Máximo EduardoSánchez Gutiérrez

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Multi-agent system approach has proven to be an effective and appropriate abstraction level to construct whole models of a diversity of biological problems, integrating aspects which can be found both in "micro" and "macro" approaches when modeling this type of phenomena. Taking into account these considerations, this paper presents the important computational characteristics to be gathered into a novel bioinformatics framework built upon a multiagent architecture. The version of the tool presented herein allows studying and exploring complex problems belonging principally to structural biology, such as protein folding. The bioinformatics framework is used as a virtual laboratory to explore a minimalist model of protein folding as a test case. In order to show the laboratory concept of the platform as well as its flexibility and adaptability, we studied the folding of two particular sequences, one of 45-mer and another of 64-mer, both described by an HP model (only hydrophobic and polar residues) and coarse grained 2D-square lattice. According to the discussion section of this piece of work, these two sequences were chosen as breaking points towards the platform, in order to determine the tools to be created or improved in such a way to overcome the needs of a particular computation and analysis of a given tough sequence. The backwards philosophy herein is that the continuous studying of sequences provides itself important points to be added into the platform, to any time improve its efficiency, as is demonstrated herein.

Keywords: multi-agent systems, blackboard-based agent architecture, bioinformatics framework, virtual laboratory, protein folding.

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Authors: B. O. Diyaolu, E. A. Okebanjo

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Children in today’s world need attention and care even with their physique as obesity is also at the increased. Several factors can be responsible for obesity in children and adequate attention is paramount in other not to accommodate it into adolescent period. This study investigated perceived determinants of obesity among primary school pupils in Eti Osa Local Government area of Lagos State. Descriptive survey research design was used and population was all obese pupils in Eti Osa Local Government Area of Lagos State. 92 pupils were selected from randomly picked 12 primary schools while purposive sampling technique was used to pick primary 4-6 pupils. With the aid of body mass index (BMI) and age percentile chart the obese pupils were selected. The instrument for the study was a self-developed and structured questionnaire on perceived determinant of obesity. The questionnaire was divided into three sections. The Cronbach’s Alpha reliability coefficient of 0.74 was obtained. The hypotheses were tested at 0.05 significant levels. The completed questionnaire was collated coded and analyzed using descriptive statistics of frequency counts and percentage and inferential statistics of chi-square (X²). Findings of this study revealed that physical activities and parental influences were determinant of obesity. Physical activity is essential in reducing the rate of obesity in Eti Osa Local Government Area both at home and within the school environment. Primary schools need to create more playing ground for pupils to exercise themselves. Parents need to cater for their children diet ensuring not just the quantity but the quality as well.

Keywords: Feeding pattern, obese pupils, parental influence, physical activities.

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Authors: N. Cetinkaya, N. H. Ozdemir

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The aim of the study was to investigate the effect of Saccharomyces cerevisiae (SC) live yeast culture on microbial protein supply to small intestine in Kivircik male yearlings when fed with different ratio of forage and concentrate diets. Four Kivircik male yearlings with permanent rumen canula were used in the experiment. The treatments were allocated to a 4x4 Latin square design. Diet I consisted of 70% alfalfa hay and 30% concentrate, Diet II consisted of 30% alfalfa hay and 70% concentrate, Diet I and II were supplemented with a SC. Daily urine was collected and stored at -20°C until analysis. Calorimetric methods were used for the determination of urinary allantoin and creatinine levels. The estimated microbial N supply to small intestine for Diets I, I+SC, II and II+SC were 2.51, 2.64, 2.95 and 3.43 g N/d respectively. Supplementation of Diets I and II with SC significantly affected the allantoin levels in μmol/W0.75 (p<0.05). Mean creatinine values in μmol/W0.75 and allantoin:creatinine ratios were not significantly different among diets. In conclusion, supplementation with SC live yeast culture had a significant effect on urinary allantoin excretion and microbial protein supply to small intestine in Kivircik yearlings fed with high concentrate Diet II (P<0.05). Hence urinary allantoin excretion may be used as a tool for estimating microbial protein supply in Kivircık yearlings. However, further studies are necessary to understand the metabolism of Saccharomyces cerevisiae live yeast culture with different forage:concentrate ratio in Kıvırcık Yearlings.

Keywords: Allantoin, creatinine, Kivircik yearling, microbial nitrogen, Saccharomyces cerevisiae.

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Authors: Y. S. Liew, K. A. Puteh Ariffin, M. A. Mohd Nor

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One of the Best Management Practices (BMPs) promoted in Urban Stormwater Management Manual for Malaysia (MSMA) published by the Department of Irrigation and Drainage (DID) in 2001 is through the construction of wet ponds in new development projects for water quantity and quality control. Therefore, this paper aims to demonstrate a case study on evaluation of a constructed mini wet pond located at Sekolah Rendah Kebangsaan Seksyen 2, Puchong, Selangor, Malaysia in both stormwater quantity and quality aspect particularly to reduce the peak discharge by temporary storing and gradual release of stormwater runoff from an outlet structure or other release mechanism. The evaluation technique will be using InfoWorks Collection System (CS) as the numerical modeling approach for water quantity aspect. Statistical test by comparing the correlation coefficient (R²), mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate the model in simulating the peak discharge changes. Results demonstrated that there will be a reduction in peak flow at 11 % to 15% and time to peak flow is slower by 5 minutes through a wet pond. For water quality aspect, a survey on biological indicator of water quality carried out depicts that the pond is within the range of rather clean to clean water with the score of 5.3. This study indicates that a constructed wet pond with wetland facilities is able to help in managing water quantity and stormwater generated pollution at source, towards achieving ecologically sustainable development in urban areas.

Keywords: Wet pond, Retention Facilities, Best Management Practices (BMP), Urban Stormwater Management Manual for Malaysia (MSMA).

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Authors: P. I. Adzongo, O. A. Aloga

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The purpose of the study was to examine lifelong education for teachers as a tool for achieving effective teaching and learning. Lifelong education enhances social inclusion, personal development, citizenship, employability, teaching and learning, community and the nation. It is imperative that the teacher needs to update his knowledge regularly to be able to perform optimally, since he has a major position in the inculcation of desirable elements in students, and the challenges of lifelong education were also discussed. Descriptive survey design was adopted for the study. A simple random sampling technique was used to select 80 teachers as sample from a population of 105 senior secondary school teachers in Makurdi Local Government Area of Benue State. A 20-item self designed questionnaire subjected to expert validation and reliability was used to collect data. The reliability Alpha coefficient of 0.87 was established using Cronbach’s Alpha technique, mean scores and standard deviation were used to answer the 2 research questions while chi-square was used to analyse data for the 2 null hypotheses, which states that lifelong education for teachers is not a significant tool for achieving effective teaching and lifelong education for teachers does not significantly impact on effective learning. The findings of the study revealed that, lifelong education for teachers can be used as a tool for achieving effective teaching and learning, and the study recommended among others that government, organizations and individuals should in collaboration put lifelong education programmes for teachers on the priority list. The paper concluded that the strategic position of lifelong education for teachers towards enhanced teaching, learning and the production of quality manpower in the society makes it imperative for all hands to be on “deck” to support the programme financially and otherwise.

Keywords: Lifelong Education, Tool, Effective Teaching and Learning.

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Authors: Mayowa Adeyeye

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This study was conducted to investigate the psychological variables motivating sport participation and involvement among student-athletes of tertiary institutions in southwest Nigeria. One thousand three hundred and fifty (N-1350) studentathletes were randomly selected in all sports from nine tertiary institutions in south-west Nigeria. These tertiary institutions include University of Lagos, Lagos State University, Obafemi Awolowo University, Osun State University, University of Ibadan, University of Agriculture Abeokuta, Federal University of Technology Akungba, University of Ilorin, and Kwara State University. The descriptive survey research method was adopted while a self developed validated Likert type questionnaire named Sport Participation Scale (SPS) was used to elicit opinion from respondents. The test-retest reliability value obtained for the instrument, using Pearson Product Moment Correlation Co-efficient was 0.96. Out of the one thousand three hundred and fifty (N-1350) questionnaire administered, only one thousand two hundred and five (N-1286) were correctly filled, coded and analysed using inferential statistics of Chi-Square (X2) while all the tested hypotheses were set at. 05 alpha level. Based on the findings of this study, the result revealed that several psychological factors influence student athletes to continue participation in sport one which includes love for the game, famous athletes as role model and family support. However, the analysis further revealed that the stipends the student-athletes get from their universities have no influence on their participation and involvement in sport.

Keywords: Family support, peer, role model, sport participation, student-athletes.

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

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Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: Climate change, coastal vulnerability index, global warming, sea level rise.

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Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores, Valentin Soloiu

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This work describes a system that uses electromyography (EMG) signals obtained from muscle sensors and an Artificial Neural Network (ANN) for signal classification and pattern recognition that is used to control a small unmanned aerial vehicle using specific arm movements. The main objective of this endeavor is the development of an intelligent interface that allows the user to control the flight of a drone beyond direct manual control. The sensor used were the MyoWare Muscle sensor which contains two EMG electrodes used to collect signals from the posterior (extensor) and anterior (flexor) forearm, and the bicep. The collection of the raw signals from each sensor was performed using an Arduino Uno. Data processing algorithms were developed with the purpose of classifying the signals generated by the arm’s muscles when performing specific movements, namely: flexing, resting, and motion of the arm. With these arm motions roll control of the drone was achieved. MATLAB software was utilized to condition the signals and prepare them for the classification. To generate the input vector for the ANN and perform the classification, the root mean square and the standard deviation were processed for the signals from each electrode. The neuromuscular information was trained using an ANN with a single 10 neurons hidden layer to categorize the four targets. The result of the classification shows that an accuracy of 97.5% was obtained. Afterwards, classification results are used to generate the appropriate control signals from the computer to the drone through a Wi-Fi network connection. These procedures were successfully tested, where the drone responded successfully in real time to the commanded inputs.

Keywords: Biosensors, electromyography, Artificial Neural Network, Arduino, drone flight control, machine learning.

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Authors: János Levendovszky, András Oláh

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In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.

Keywords: Cellular Neural Network, channel equalization, communication over fading channels, multiuser communication, spectral efficiency, statistical sampling.

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Authors: Muhammad Aqil, Ichiro Kita, Moses Macalinao

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Reliable water level forecasts are particularly important for warning against dangerous flood and inundation. The current study aims at investigating the suitability of the adaptive network based fuzzy inference system for continuous water level modeling. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the network. For this study, water levels data are available for a hydrological year of 2002 with a sampling interval of 1-hour. The number of antecedent water level that should be included in the input variables is determined by two statistical methods, i.e. autocorrelation function and partial autocorrelation function between the variables. Forecasting was done for 1-hour until 12-hour ahead in order to compare the models generalization at higher horizons. The results demonstrate that the adaptive networkbased fuzzy inference system model can be applied successfully and provide high accuracy and reliability for river water level estimation. In general, the adaptive network-based fuzzy inference system provides accurate and reliable water level prediction for 1-hour ahead where the MAPE=1.15% and correlation=0.98 was achieved. Up to 12-hour ahead prediction, the model still shows relatively good performance where the error of prediction resulted was less than 9.65%. The information gathered from the preliminary results provide a useful guidance or reference for flood early warning system design in which the magnitude and the timing of a potential extreme flood are indicated.

Keywords: Neural Network, Fuzzy, River, Forecasting

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Authors: Theerawat Songyot, Tham Manjing, Bunyarit Uyyanonvara, Chanjira Sinthanayothin

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Corner detection and optical flow are common techniques for feature-based video stabilization. However, these algorithms are computationally expensive and should be performed at a reasonable rate. This paper presents an algorithm for discarding irrelevant feature points and maintaining them for future use so as to improve the computational cost. The algorithm starts by initializing a maintained set. The feature points in the maintained set are examined against its accuracy for modeling. Corner detection is required only when the feature points are insufficiently accurate for future modeling. Then, optical flows are computed from the maintained feature points toward the consecutive frame. After that, a motion model is estimated based on the simplified affine motion model and least square method, with outliers belonging to moving objects presented. Studentized residuals are used to eliminate such outliers. The model estimation and elimination processes repeat until no more outliers are identified. Finally, the entire algorithm repeats along the video sequence with the points remaining from the previous iteration used as the maintained set. As a practical application, an efficient video stabilization can be achieved by exploiting the computed motion models. Our study shows that the number of times corner detection needs to perform is greatly reduced, thus significantly improving the computational cost. Moreover, optical flow vectors are computed for only the maintained feature points, not for outliers, thus also reducing the computational cost. In addition, the feature points after reduction can sufficiently be used for background objects tracking as demonstrated in the simple video stabilizer based on our proposed algorithm.

Keywords: background object tracking, feature point reduction, low cost tracking, video stabilization.

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Authors: M. F. Nawaz, S. Gul, T. H. Farooq, M. T. Siddiqui, M. Asif, I. Ahmad, N. K. Niazi

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In Pakistan, major demands of fuel wood and timber wood are fulfilled by agroforestry. However, the information regarding economic significance of agroforestry and its productivity in Pakistan is still insufficient and unreliable. Survey of field conditions to examine the agroforestry status at local level helps us to know the future trends and to formulate the policies for sustainable wood supply. The objectives of this research were to examine the actual status and potential of agroforestry and to point out the barriers that are faced by farmers in the adoption of agroforestry. Research was carried out in Chiniot district, Pakistan because it is the famous city for furniture industry that is largely dependent on farm trees. A detailed survey of district Chiniot was carried out from 150 randomly selected farmer respondents using multi-objective oriented and pre-tested questionnaire. It was found that linear tree planting method was more adopted (45%) as compared to linear + interplanting (42%) and/or compact planting (12.6%). Chi-square values at P-value <0.5 showed that age (11.35) and education (17.09) were two more important factors in the quick adoption of agroforestry as compared to land holdings (P-value of 0.7). The major reason of agroforestry adoption was to obtain income, fodder and fuelwood. The most dominant species in farmlands was shisham (Dalbergia sissoo) but since last five years, mostly farmers were growing Sufeida (Eucalyptus camaldulensis), kikar (Acacia nilotica) and popular (Populus deltoides) on their fields due to “Shisham die-back” problem. It was found that agro-forestry can be increased by providing good quality planting material to farmers and improving wood markets.

Keywords: Agroforestry, trees, services, agriculture, farmers.

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