Search results for: initial teacher training

Authors: Reza Bazargan-Lari, Mohammad Ebrahim Bahrololoom, Afshin Nemati

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Heavy metals have bad effects on environment and soils and it can uptake by natural HAP .natural Hap is an inexpensive material that uptake large amounts of various heavy metals like Zn (II) .Natural HAP (N-HAP), extracted from bovine cortical bone ash, is a good choice for substitution of commercial HAP. Several experiments were done to investigate the sorption capacity of Zn (II) to N-HAP in various particles sizes, temperatures, initial concentrations, pH and reaction times. In this study, the sorption of Zinc ions from a Zn solution onto HAP particles with sizes of 1537.6 nm and 47.6 nm at three initial pH values of 4.50, 6.00 and 7.50 was studied. The results showed that better performance was obtained through a 47.6 nm particle size and higher pH values. The experimental data were analyzed using Langmuir, Freundlich, and Arrhenius equations for equilibrium, kinetic and thermodynamic studies. The analysis showed a maximum adsorption capacity of NHAP as being 1.562 mmol/g at a pH of 7.5 and small particle size. Kinetically, the prepared N-HAP is a feasible sorbent that retains Zn (II) ions through a favorable and spontaneous sorption process.

Keywords: Natural Hydroxyapatite, Heavy metal ions, Adsorption, Zn removal, kinetic model, bone ash

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Authors: B. Ghanbarian-Alavijeh, A.M. Liaghat, S. Sohrabi

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Saturated hydraulic conductivity is one of the soil hydraulic properties which is widely used in environmental studies especially subsurface ground water. Since, its direct measurement is time consuming and therefore costly, indirect methods such as pedotransfer functions have been developed based on multiple linear regression equations and neural networks model in order to estimate saturated hydraulic conductivity from readily available soil properties e.g. sand, silt, and clay contents, bulk density, and organic matter. The objective of this study was to develop neural networks (NNs) model to estimate saturated hydraulic conductivity from available parameters such as sand and clay contents, bulk density, van Genuchten retention model parameters (i.e. r θ , α , and n) as well as effective porosity. We used two methods to calculate effective porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s θ is saturated water content, FC θ is water content retained at -33 kPa matric potential, and inf θ is water content at the inflection point. Total of 311 soil samples from the UNSODA database was divided into three groups as 187 for the training, 62 for the validation (to avoid over training), and 62 for the test of NNs model. A commercial neural network toolbox of MATLAB software with a multi-layer perceptron model and back propagation algorithm were used for the training procedure. The statistical parameters such as correlation coefficient (R2), and mean square error (MSE) were also used to evaluate the developed NNs model. The best number of neurons in the middle layer of NNs model for methods (1) and (2) were calculated 44 and 6, respectively. The R2 and MSE values of the test phase were determined for method (1), 0.94 and 0.0016, and for method (2), 0.98 and 0.00065, respectively, which shows that method (2) estimates saturated hydraulic conductivity better than method (1).

Keywords: Neural network, Saturated hydraulic conductivity, Soil physical properties.

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Authors: M. Ghalambaz, A.R. Noghrehabadi, M.A. Behrang, E. Assareh, A. Ghanbarzadeh, N.Hedayat

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This study presents a hybrid neural network and Gravitational Search Algorithm (HNGSA) method to solve well known Wessinger's equation. To aim this purpose, gravitational search algorithm (GSA) technique is applied to train a multi-layer perceptron neural network, which is used as approximation solution of the Wessinger's equation. A trial solution of the differential equation is written as sum of two parts. The first part satisfies the initial/ boundary conditions and does not contain any adjustable parameters and the second part which is constructed so as not to affect the initial/boundary conditions. The second part involves adjustable parameters (the weights and biases) for a multi-layer perceptron neural network. In order to demonstrate the presented method, the obtained results of the proposed method are compared with some known numerical methods. The given results show that presented method can introduce a closer form to the analytic solution than other numerical methods. Present method can be easily extended to solve a wide range of problems.

Keywords: Neural Networks, Gravitational Search Algorithm (GSR), Wessinger's Equation.

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Authors: M A Hannan, A. Hussain, S. A. Samad, K. A. Ishak, A. Mohamed

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This paper presents a general trainable framework for fast and robust upright human face and non-human object detection and verification in static images. To enhance the performance of the detection process, the technique we develop is based on the combination of fast neural network (FNN) and classical neural network (CNN). In FNN, a useful correlation is exploited to sustain high level of detection accuracy between input image and the weight of the hidden neurons. This is to enable the use of Fourier transform that significantly speed up the time detection. The combination of CNN is responsible to verify the face region. A bootstrap algorithm is used to collect non human object, which adds the false detection to the training process of the human and non-human object. Experimental results on test images with both simple and complex background demonstrate that the proposed method has obtained high detection rate and low false positive rate in detecting both human face and non-human object.

Keywords: Algorithm, detection of human and non-human object, FNN, CNN, Image training.

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Authors: Ángel de J. López-Pérez, Sonia E. Ruiz, Vanessa A. Segovia

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Buildings vulnerability due to seismic activity has been highly studied since the middle of last century. As a solution to the structural and non-structural damage caused by intense ground motions, several seismic energy dissipating devices, such as buckling-restrained braces (BRB), have been proposed. BRB have shown to be effective in concentrating a large portion of the energy transmitted to the structure by the seismic ground motion. A design approach for buildings with BRB elements, which is based on a seismic Displacement-Based formulation, has recently been proposed by the coauthors in this paper. It is a practical and easy design method which simplifies the work of structural engineers. The method is used here for the design of the structure-BRB damper system. The objective of the present study is to extend and apply a methodology to find the best combination of design parameters on multiple-degree-of-freedom (MDOF) structural frame – BRB systems, taking into account simultaneously: 1) initial costs and 2) an adequate engineering demand parameter. The design parameters considered here are: the stiffness ratio (α = K_frame/K_total), and the strength ratio (γ = V_damper/V_total); where K represents structural stiffness and V structural strength; and the subscripts "frame", "damper" and "total" represent: the structure without dampers, the BRB dampers and the total frame-damper system, respectively. The selection of the best combination of design parameters α and γ is based on an initial costs analysis and on the structural dynamic response of the structural frame-damper system. The methodology is applied to a 12-story 5-bay steel building with BRB, which is located on the intermediate soil of Mexico City. It is found the best combination of design parameters α and γ for the building with BRB under study.

Keywords: Best combination of design parameters, BRB, buildings with energy dissipating devices, buckling-restrained braces, initial costs.

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: Distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: Jimaima Lako, Subramaniam Sotheeswaran, Ketan Christi

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The 15-a-side Fiji rugby team trains well in preparations for any rugby competition but rarely performs to expectations. In order to help the Fiji local based rugby players to identify some key basic areas in improving their performance, a series of workshops were conducted to assess their nutritional status and dietary habits in relation to energy demand during rugby matches. The nutrition workshop included the administration of questionnaires to 19 local based rugby players, requesting the following information: usual food intakes, training camp food intakes, carbohydrate loading, pre-game meals and post-game meals. The study revealed that poor eating habits of the players resulted in the low carbohydrate intake, which may have contributed to increase levels of fatigue leading to loss of stamina even before the second half of the game. It appears that the diet of most 15-a-side players does not provide enough energy to enable them to last the full eightyminutes of the game.

Keywords: Fiji rugby, Food habits, Physical fitness, Training meals

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Authors: M. S. Mahmoud

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This study was conducted to evaluate the manganese removal from aqueous solution using Banana peels activated carbon (BPAC). Batch experiments have been carried out to determine the influence of parameters such as pH, biosorbent dose, initial metal ion concentrations and contact times on the biosorption process. From these investigations, a significant increase in percentage removal of manganese 97.4% is observed at pH value 5.0, biosorbent dose 0.8 g, initial concentration 20 ppm, temperature 25 ± 2°C, stirring rate 200 rpm and contact time 2h. The equilibrium concentration and the adsorption capacity at equilibrium of the experimental results were fitted to the Langmuir and Freundlich isotherm models; the Langmuir isotherm was found to well represent the measured adsorption data implying BPAC had heterogeneous surface. A raw groundwater samples were collected from Baharmos groundwater treatment plant network at Embaba and Manshiet Elkanater City/District-Giza, Egypt, for treatment at the best conditions that reached at first phase by BPAC. The treatment with BPAC could reduce iron and manganese value of raw groundwater by 91.4% and 97.1%, respectively and the effect of the treatment process on the microbiological properties of groundwater sample showed decrease of total bacterial count either at 22°C or at 37°C to 85.7% and 82.4%, respectively. Also, BPAC was characterized using SEM and FTIR spectroscopy.

Keywords: Biosorption, banana peels, isothermal models, manganese.

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Authors: Natthawan Likitmongkonsakun, Tawan Limpiyakorn

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17α-ethynylestradiol (EE2) is a synthetic estrogen used as a key ingredient in an oral contraceptives pill. EE2 is an endocrine disrupting compound, high in estrogenic potency. Although EE2 exhibits low degree of biodegradability with common microorganisms in wastewater treatment plants (WWTPs), this compound can be biotransformed by ammonia-oxidizing bacteria (AOB) via a co-metabolism mechanism in WWTPs. This study aimed to investigate the effect of real wastewater on biotransformation of EE2 by AOB. A preliminary experiment on the effect of nitrite and pH levels on abiotic transformation of EE2 suggested that the abiotic transformation occurred at only pH <6.8. Biotransformation of EE2 under the presence of municipal or industrial wastewater demonstrated that different types of wastewater affect EE2 biotransformation differently. Organic matters in wastewater were believed to deteriorate EE2 biotransformation via the competition effect. At a lower initial ammonium concentration, EE2 biotransformation can be retarded and the extent of the deterioration was COD-concentration dependent. However, when an initial ammonium concentration was elevated, thisphenomena disappeared. This is because when increasing the amount of the primary substrate, more AMO enzymes can be produced resulting in unlimited transformation of all compounds in the tests reducing the competitive effect of organic matters on EE2.

Keywords: 17α-ethynylestradiol, ammonia-oxidizing bacteria, nitrifying activated sludge, wastewater.

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Authors: A. Srion, W. Thepsuwan, N. Monmaturapoj

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Two Lithium Disilicate (LD) glass ceramics based on SiO2-Li2O-K2O-Al2O3 system were prepared through a glass melting method. The glass rods were then fabricated into dental crowns via a hot pressing at 900˚C and 850˚C in order to study the effect of the pressing temperatures on the phase formation and microstructure of the glasses. Different samples of as cast glass and heat treated samples (600˚C and 700˚C) were used to press for investigating the effect of an initial microstructure on the hot pressing technique. Xray diffraction (XRD) and scanning electron microscopy (SEM) were performed to determine the phase formation and microstructure of the samples, respectively. XRD results show that the main crystalline structure was Li2Si2O5 by having Li3PO4, Li0.6Al0.6Si2O6, Li2SiO3, Ca5 (PO4)3F and SiO2 as minor phases. Glass compositions with different heat treatment temperatures exhibited a difference phase formations but have less effect during pressing. SEM micrographs showed the microstructure of Li2Si2O5 as lath-like shape in all glasses. With increasing the initial heat treatment temperature, the longer the lath-like crystals of lithium disilicate were increased especially when using glass heat treatment at 700˚C followed by pressing at 900˚C. This could be suggested that LD1 heat treatment at 700˚C which pressing at 900˚C presented the best formation by the hot pressing and compiled microstructure.

Keywords: Lithium disilicate, Hot pressing, Dental crown, Microstructure.

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Authors: Maciej Zasuwa, Grzegorz Ptasinski, Antoni Kopyt

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Nowadays flight simulators offer tremendous possibilities for safe and cost-effective pilot training, by utilization of powerful, computational tools. Due to technology outpacing methodology, vast majority of training related work is done by human instructors. It makes assessment not efficient, and vulnerable to instructors’ subjectivity. The research presents an Objective Assessment Tool (gOAT) developed at the Warsaw University of Technology, and tested on SW-4 helicopter flight simulator. The tool uses database of the predefined manoeuvres, defined and integrated to the virtual environment. These were implemented, basing on Aeronautical Design Standard Performance Specification Handling Qualities Requirements for Military Rotorcraft (ADS-33), with predefined Mission-Task-Elements (MTEs). The core element of the gOAT enhanced algorithm that provides instructor a new set of information. In details, a set of objective flight parameters fused with report about psychophysical state of the pilot. While the pilot performs the task, the gOAT system automatically calculates performance using the embedded algorithms, data registered by the simulator software (position, orientation, velocity, etc.), as well as measurements of physiological changes of pilot’s psychophysiological state (temperature, sweating, heart rate). Complete set of measurements is presented on-line to instructor’s station and shown in dedicated graphical interface. The presented tool is based on open source solutions, and flexible for editing. Additional manoeuvres can be easily added using guide developed by authors, and MTEs can be changed by instructor even during an exercise. Algorithm and measurements used allow not only to implement basic stress level measurements, but also to reduce instructor’s workload significantly. Tool developed can be used for training purpose, as well as periodical checks of the aircrew. Flexibility and ease of modifications allow the further development to be wide ranged, and the tool to be customized. Depending on simulation purpose, gOAT can be adjusted to support simulator of aircraft, helicopter, or unmanned aerial vehicle (UAV).

Keywords: Automated assessment, flight simulator, human factors, pilot training.

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Authors: H. C. O. Reis, A. S. Cossolin, B. A. P. Santos, K. C. Castro, G. M. Pereira, V. C. Silva, P. T. Sousa Jr, E. L. Dall’Oglio, L. G. Vasconcelos, E. B. Morais

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In this study, malt bagasse, a low-cost waste biomass, was tested as a biosorbent to remove the cationic dye Malachite green (MG) from aqueous solution. Batch biosorption experiments were investigated as functions of different experimental parameters such as initial pH, salt (NaCl) concentration, contact time, temperature and initial dye concentration. Higher removal rates of MG were obtained at pH 8 and 10. The equilibrium and kinetic studies suggest that the biosorption follows Langmuir isotherm and the pseudo-second-order model. The maximum monolayer adsorption capacity was estimated at 117.65 mg/g (at 45 °C). According to Dubinin–Radushkevich (D-R) isotherm model, biosorption of MG onto malt bagasse occurs physically. The thermodynamic parameters such as Gibbs free energy, enthalpy and entropy indicated that the MG biosorption onto malt bagasse is spontaneous and endothermic. The results of the ionic strength effect indicated that the biosorption process under study had a strong tolerance under high salt concentrations. It can be concluded that malt bagasse waste has potential for application as biosorbent for removal of MG from aqueous solution.

Keywords: Color removal, kinetic and isotherm studies, thermodynamic parameters, FTIR.

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Authors: D. Zare, H. Naderi, A. A. Jafari

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Drying characteristics of rough rice (variety of lenjan) with an initial moisture content of 25% dry basis (db) was studied in a hot air dryer assisted by infrared heating. Three arrival air temperatures (30, 40 and 500C) and four infrared radiation intensities (0, 0.2 , 0.4 and 0.6 W/cm2) and three arrival air speeds (0.1, 0.15 and 0.2 m.s-1) were studied. Bending strength of brown rice kernel, percentage of cracked kernels and time of drying were measured and evaluated. The results showed that increasing the drying arrival air temperature and radiation intensity of infrared resulted decrease in drying time. High bending strength and low percentage of cracked kernel was obtained when paddy was dried by hot air assisted infrared dryer. Between this factors and their interactive effect were a significant difference (p<0.01). An intensity level of 0.2 W/cm2 was found to be optimum for radiation drying. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the moisture content during drying (output parameter for ANN modeling) was investigated. Infrared Radiation intensity, drying air temperature, arrival air speed and drying time were considered as input parameters for the model. An ANN model with two hidden layers with 8 and 14 neurons were selected for studying the influence of transfer functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent sigmoid) transfer function and trainlm (Levenberg-Marquardt) back propagation algorithm made the most accurate predictions for the paddy drying system. Mean square error (MSE) was calculated and found that the random errors were within and acceptable range of ±5% with coefficient of determination (R2) of 99%.

Keywords: Rough rice, Infrared-hot air, Artificial Neural Network

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Authors: Behrouz Mosayebi Dehkordi

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Simultaneous effects of temperature, immersion time, salt concentration, sucrose concentration, pressure and convective dryer temperature on the combined osmotic dehydration - convective drying of edible button mushrooms were investigated. Experiments were designed according to Central Composite Design with six factors each at five different levels. Response Surface Methodology (RSM) was used to determine the optimum processing conditions that yield maximum water loss and rehydration ratio and minimum solid gain and shrinkage in osmotic-convective drying of edible button mushrooms. Applying surfaces profiler and contour plots optimum operation conditions were found to be temperature of 39 °C, immersion time of 164 min, salt concentration of 14%, sucrose concentration of 53%, pressure of 600 mbar and drying temperature of 40 °C. At these optimum conditions, water loss, solid gain, rehydration ratio and shrinkage were found to be 63.38 (g/100 g initial sample), 3.17 (g/100 g initial sample), 2.26 and 7.15%, respectively.

Keywords: Dehydration, Mushroom, Optimization, Osmotic, Response Surface Methodology

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Authors: Behrouz Mosayebi Dehkordi

Abstract:

Simultaneous effects of temperature, immersion time, salt concentration, sucrose concentration, pressure and convective dryer temperature on the combined osmotic dehydration - convective drying of edible button mushrooms were investigated. Experiments were designed according to Central Composite Design with six factors each at five different levels. Response Surface Methodology (RSM) was used to determine the optimum processing conditions that yield maximum water loss and rehydration ratio and minimum solid gain and shrinkage in osmotic-convective drying of edible button mushrooms. Applying surfaces profiler and contour plots optimum operation conditions were found to be temperature of 39 °C, immersion time of 164 min, salt concentration of 14%, sucrose concentration of 53%, pressure of 600 mbar and drying temperature of 40 °C. At these optimum conditions, water loss, solid gain, rehydration ratio and shrinkage were found to be 63.38 (g/100 g initial sample), 3.17 (g/100 g initial sample), 2.26 and 7.15%, respectively.

Keywords: Dehydration, mushroom, optimization, osmotic, response surface methodology.

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Authors: T. Ito, K. Kawaguchi, R. Ohta

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The purpose of this study is to provide a guideline to assist globally-minded companies in developing task-based English- language programs for their employees. After conducting an online self-assessment questionnaire comprised of 45 job-related tasks, we analyzed responses received from 3,000 Japanese company employees and developed a checklist that considered three areas; i) the percentage of those who need to accomplish English-language tasks in their workplace (need for English), ii) a five-point self-assessment score (task performance level), and iii) the impact of previous task experience on perceived performance (experience factor). The 45 tasks were graded according to five proficiency levels. Our results helped us to create a core guideline that may assist companies in two ways: first, in helping determine which tasks employees with a certain English proficiency should be able to satisfactorily carry out, and secondly, to quickly prioritize which business-related English skills they would need in future English language programs.

Keywords: Business settings, Can-do statements, English language training programs, Self-assessment, Task experience.

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Authors: Ajay Shekhar Pandey, D. Singh, S. K. Sinha

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A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.

Keywords: Load forecasting, clustering, fuzzy inference.

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Authors: Hsiu-Hua Cheng

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Recently, the competition between websites becomes intense. How to make users “adopt” their websites is an issue of urgent importance for online communities companies. Social procedures (such as social influence) can possibly explain how and why users’ technologies usage behaviors affect other people to use the technologies. This study proposes two types of social influences on the initial usage of Facebook Check In-friends and group members. Besides, this study combines social influences theory and social network theory to explore the factors influencing initial usage of Facebook Check In. This study indicates that Facebook friends’ previous usage of Facebook Check In and Facebook group members’ previous usage of Facebook Check In will positively influence focal actors’ Facebook Check In adoption intention, and network centrality will moderate the relationships among Facebook friends’ previous usage of Facebook Check In, Facebook group members’ previous usage of Facebook Check In and focal actors’ Facebook Check In adoption intention. The article concludes with contributions to academic research and practice.

Keywords: Social Influence, Adoption Intention, Facebook Check In, Previous Usage behavior.

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Authors: Mohamed Abdallah, Mostafa Warith, Roberto Narbaitz, Emil Petriu, Kevin Kennedy

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Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.

Keywords: Adaptive neural fuzzy inference system (ANFIS), gas production, landfill

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Authors: Wei Hua Ho

Abstract:

Vortices can develop in intakes of turbojet and turbo fan aero engines during high power operation in the vicinity of solid surfaces. These vortices can cause catastrophic damage to the engine. The factors determining the formation of the vortex include both geometric dimensions as well as flow parameters. It was shown that the threshold at which the vortex forms or disappears is also dependent on the initial flow condition (i.e. whether a vortex forms after stabilised non vortex flow or vice-versa). A computational fluid dynamics study was conducted to determine the difference in thresholds between the two conditions. This is the first reported numerical investigation of the “memory effect". The numerical results reproduce the phenomenon reported in previous experimental studies and additional factors, which had not been previously studied, were investigated. They are the rate at which ambient velocity changes and the initial value of ambient velocity. The former was found to cause a shift in the threshold but not the later. It was also found that the varying condition thresholds are not symmetrical about the neutral threshold. The vortex to no vortex threshold lie slightly further away from the neutral threshold compared to the no vortex to vortex threshold. The results suggests that experimental investigation of vortex formation threshold performed either in vortex to no vortex conditions, or vice versa, solely may introduce mis-predictions greater than 10%.

Keywords: Jet Engine Test Cell, Unsteady flow, Inlet Vortex

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Authors: Tailong Zhang, Jianming Gao, Xue Xie, Wei Sun

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Compatibility between sulfonated acetone- formalehyde superplasticizer (SAF) and copolymer-based grinding aids (GA) were studied by fluidity, Zeta potential, setting time of cement pasts, initial slump and slump flow of concrete and compressive strength of concrete. ESEM, MIP, and XRD were used to investigate the changing of microstructure of interior concrete. The results indicated that GA could noticeably enhance the dispersion ability of SAF. It was found that better fluidity and slump-keeping ability of cement paste were obtained in the case of GA. In addition, GA together with SAF had a certain retardation effect on hydration of cement paste. With increasing of the GA dosage, the dispersion ability and retardation effect of admixture increased. The compressive strength of the sample made with SAF and GA after 28 days was higher than that of the control sample made only with SAF.  The initial slump and slump flow of concrete increased by 10.0% and 22.9%, respectively, while 0.09 wt.% GA was used. XRD examination indicated that new products were not found in the case of GA. In addition, more dense arrangement of hydrates and lower porosity of the specimen were observed by ESEM and MIP, which contributed to higher compressive strength.

Keywords: Copolymer-Based grinding aids, superplasiticizer, compatibility, microstructure, cement, concrete.

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Authors: Adel S. Alanazi

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The transition of a student with a disability from school to work is the most crucial phase while moving from the stage of adolescence into early adulthood. In this process, young individuals face various difficulties and challenges in order to accomplish the next venture of life successfully. In this respect, this paper aims to examine the challenges encountered by the individuals with intellectual disabilities in transition to work in Saudi Arabia. For this purpose, this study has undertaken a qualitative research-based methodology; wherein interpretivist philosophy has been followed along with inductive approach and exploratory research design. The data for the research has been gathered with the help of semi-structured interviews, whose findings are analysed with the help of thematic analysis. Semi-structured interviews were conducted with parents of persons with intellectual disabilities, officials, supervisors and specialists of two vocational rehabilitation centres providing training to intellectually disabled students, in addition to that, directors of companies and websites in hiring those individuals. The total number of respondents for the interview was 15. The purposive sampling method was used to select the respondents for the interview. This sampling method is a non-probability sampling method which draws respondents from a known population and allows flexibility and suitability in selecting the participants for the study. The findings gathered from the interview revealed that the lack of awareness among their parents regarding the rights of their children who are intellectually disabled; the lack of adequate communication and coordination between various entities; concerns regarding their training and subsequent employment are the key difficulties experienced by the individuals with intellectual disabilities. Training in programmes such as bookbinding, carpentry, computing, agriculture, electricity and telephone exchange operations were involved as key training programmes. The findings of this study also revealed that information technology and media were playing a significant role in smoothing the transition to employment of individuals with intellectual disabilities. Furthermore, religious and cultural attitudes have been identified to be restricted for people with such disabilities in seeking advantages from job opportunities. On the basis of these findings, it can be implied that the information gathered through this study will serve to be highly beneficial for Saudi Arabian schools/ rehabilitation centres for individuals with intellectual disability to facilitate them in overcoming the problems they encounter during the transition to work.

Keywords: Intellectual disability, transition services, rehabilitation centre.

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Authors: Khalid Ahmed Elrabie Abdelrasoul

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The present study was carried out on Sahiwal cattle bulls maintained at the Artificial Breeding Complex, NDRI, Karnal, Hayana, India, to assess the effect of cooling using mist cooling and fanning on Sahiwal bulls in the dry hot summer season. Fourteen Sahiwal bulls were divided into two groups of seven each. Sexual behavior and semen quality traits considered were: Reaction time (RT), Dismounting time (DMT), Total time taken in mounts (TTTM), Flehmen response (FR), Erection Score (ES), Protrusion Score (PS), Intensity of thrust (ITS), Temperament Score (TS), Libido Score (LS), Semen volume, Physical appearance, Mass activity, Initial progressive motility, Non-eosinophilic spermatozoa count (NESC) and post thaw motility percent. Data were analyzed by least squares technique. Group-1 was the control, whereas group-2 (treatment group) bulls were exposed to mist cooling and fanning (thrice a day 15 min each) in the dry hot summer season. Group-2 showed significantly (p < 0.01) higher value in DMT (sec), ES, PS, ITS, LS, semen volume (ml), semen color density, mass activity, initial motility, progressive motility and live sperm.

Keywords: Mist cooling, Sahiwal bulls, semen quality, sexual behavior.

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Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

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The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.

Keywords: Neural network computing, information processing, input-output mapping, training time, computers with high memory.

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Authors: M.A.Behrang, M. Ghalambaz, E. Assareh, A.R. Noghrehabadi

Abstract:

Fluid flow and heat transfer of vertical full cone embedded in porous media is studied in this paper. Nonlinear differential equation arising from similarity solution of inverted cone (subjected to wall temperature boundary conditions) embedded in porous medium is solved using a hybrid neural network- particle swarm optimization method. To aim this purpose, a trial solution of the differential equation is defined as sum of two parts. The first part satisfies the initial/ boundary conditions and does contain an adjustable parameter and the second part which is constructed so as not to affect the initial/boundary conditions and involves adjustable parameters (the weights and biases) for a multi-layer perceptron neural network. Particle swarm optimization (PSO) is applied to find adjustable parameters of trial solution (in first and second part). The obtained solution in comparison with the numerical ones represents a remarkable accuracy.

Keywords: Porous Media, Ordinary Differential Equations (ODE), Particle Swarm Optimization (PSO), Neural Network (NN).

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

Abstract:

Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: Intrusion detection system, decision tree, support vector machine, feature selection.

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Authors: S. Asreazad

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This paper presents the results that the soil volumetric strain and shear strength are closely related to the confining stress and initial matric suction under constant water content testing on the specimens of unsaturated sand with clay and silt fines contents. The silty sand specimens reached their peak strength after a very small axial strain followed by a post-peak softening towards an ultimate value. The post-peak drop in stress increased by an increment of the suction, while there is no peak strength for clayey sand specimens. The clayey sand shows compressibility and possesses ductile stress-strain behaviour. Shear strength increased nonlinearly with respect to matric suction for both soil types. When suction exceeds a certain range, the effect of suction on shear strength increment weakens gradually. Under the same confining stress, the dilatant tendencies in the silty sand increased under lower values of suction and decreased for higher suction values under the same confining stress. However, the amount of contraction increased with increasing initial suction for clayey sand specimens.

Keywords: Unsaturated soils, silty sand, clayey sand, triaxial test, constant water content.

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Authors: Carlos Oliveira

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This paper is a criticism of the traditional model of teaching and presents alternative teaching methods, different from the traditional lecture. These methods are accompanied by reports of experience of their application in a class. It was concluded that in the lecture, the student has a low learning rate and that other methods should be used to make the most engaging learning environment for the student, contributing (or facilitating) his learning process. However, the teacher should not use a single method, but rather a range of different methods to ensure the learning experience does not become repetitive and fatiguing for the student.

Keywords: Educational practices, experience report, IT in education, teaching methods.

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Authors: Alexander Y. Vaninsky

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The paper compares the treatment of fractions in a typical undergraduate college curriculum and in abstract algebra textbooks. It stresses that the main difference is that the undergraduate curriculum treats equivalent fractions as equal, and this treatment eventually leads to paradoxes and impairs the students- ability to perceive ratios, proportions, radicals and rational exponents adequately. The paper suggests a simplified version of rigorous theory of fractions suitable for regular college curriculum.

Keywords: Fractions, mathematics curriculum, mathematics education, teacher preparation

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Authors: Zhifeng Kong

Abstract:

Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.

Keywords: Over-parameterization, Rectified Linear Units (ReLU), convergence, gradient descent, neural networks.

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