Search results for: latent variables

Authors: Bhanu Gupta

Abstract:

In this paper, the criteria of Ψ-eventual stability have been established for generalized impulsive differential systems of multiple dependent variables. The sufficient conditions have been obtained using piecewise continuous Lyapunov function. An example is given to support our theoretical result.

Keywords: impulsive differential equations, Lyapunov function, eventual stability

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Authors: Raja Singh Khela, R. K. Bansal, K. S. Sandhu, A. K. Goel

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Self-Excited Induction Generator (SEIG) builds up voltage while it enters in its magnetic saturation region. Due to non-linear magnetic characteristics, the performance analysis of SEIG involves cumbersome mathematical computations. The dependence of air-gap voltage on saturated magnetizing reactance can only be established at rated frequency by conducting a laboratory test commonly known as synchronous run test. But, there is no laboratory method to determine saturated magnetizing reactance and air-gap voltage of SEIG at varying speed, terminal capacitance and other loading conditions. For overall analysis of SEIG, prior information of magnetizing reactance, generated frequency and air-gap voltage is essentially required. Thus, analytical methods are the only alternative to determine these variables. Non-existence of direct mathematical relationship of these variables for different terminal conditions has forced the researchers to evolve new computational techniques. Artificial Neural Networks (ANNs) are very useful for solution of such complex problems, as they do not require any a priori information about the system. In this paper, an attempt is made to use cascaded neural networks to first determine the generated frequency and magnetizing reactance with varying terminal conditions and then air-gap voltage of SEIG. The results obtained from the ANN model are used to evaluate the overall performance of SEIG and are found to be in good agreement with experimental results. Hence, it is concluded that analysis of SEIG can be carried out effectively using ANNs.

Keywords: Self-Excited Induction Generator, Artificial NeuralNetworks, Exciting Capacitance and Saturated magnetizingreactance.

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Authors: Etienne Provencal, David L. St-Pierre

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A facility exploiting only electronic gambling machines (EGMs) opened in 2007 in Quebec City, Canada under the name of Salons de Jeux du Québec (SdjQ). This facility is one of the first worldwide to rely on that business model. This paper models the performance of such EGMs. The interest from a managerial point of view is to identify the variables that can be controlled or influenced so that a comprehensive model can help improve the overall performance of the business. The EGM individual performance model contains eight different variables under study (Game Title, Progressive jackpot, Bonus Round, Minimum Coin-in, Maximum Coin-in, Denomination, Slant Top and Position). Using data from Quebec City’s SdjQ, a linear regression analysis explains 90.80% of the EGM performance. Moreover, results show a behavior slightly different than that of a casino. The addition of GameTitle as a factor to predict the EGM performance is one of the main contributions of this paper. The choice of the game (GameTitle) is very important. Games having better position do not have significantly better performance than games located elsewhere on the gaming floor. Progressive jackpots have a positive and significant effect on the individual performance of EGMs. The impact of BonusRound on the dependent variable is significant but negative. The effect of Denomination is significant but weakly negative. As expected, the Language of an EGMS does not impact its individual performance. This paper highlights some possible improvements by indicating which features are performing well. Recommendations are given to increase the performance of the EGMs performance.

Keywords: EGM, linear regression, model prediction, slot operations.

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Authors: Youngjae Jin, Daeshik Kim

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This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in VerilogHDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.

Keywords: Auto-encoder, Behavior model simulation, Digital hardware design, Pre-route simulation, Unsupervised feature learning.

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Authors: Yuri Boiko, Ci Lin, Iluju Kiringa, Tet Yeap

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The performance of a permanent magnet brushless direct current (BLDC) motor controlled by the Kalman filter based position-sensorless drive is studied in terms of its dependence from the system’s parameters variations. The effects of the system’s parameters changes on the dynamic behavior of state variables are verified. Simulated is the closed loop control scheme with Kalman filter in the feedback line. Distinguished are two separate data sampling modes in analyzing feedback output from the BLDC motor: (1) equal angular separation and (2) equal time intervals. In case (1), the data are collected via equal intervals  of rotor’s angular position i, i.e. keeping  = const. In case (2), the data collection time points ti are separated by equal sampling time intervals t = const. Demonstrated are the effects of the parameters changes on the sensorless control flow, in particular, reduction of the instability torque ripples, switching spikes, and torque load balancing. It is specifically shown that an efficient suppression of commutation induced instability torque ripples is an achievable selection of the sampling rate in the Kalman filter settings above a certain critical value. The computational cost of such suppression is shown to be higher for the motors with lower induction values of the windings.

Keywords: BLDC motor, Kalman filter, sensorless drive, state variables, instability torque ripples reduction, sampling rate.

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Authors: S. Boria

Abstract:

In the last years, the crashworthiness of an automotive body structure can be improved, since the beginning of the design stage, thanks to the development of specific optimization tools. It is well known how the finite element codes can help the designer to investigate the crashing performance of structures under dynamic impact. Therefore, by coupling nonlinear mathematical programming procedure and statistical techniques with FE simulations, it is possible to optimize the design with reduced number of analytical evaluations. In engineering applications, many optimization methods which are based on statistical techniques and utilize estimated models, called meta-models, are quickly spreading. A meta-model is an approximation of a detailed simulation model based on a dataset of input, identified by the design of experiments (DOE); the number of simulations needed to build it depends on the number of variables. Among the various types of meta-modeling techniques, Kriging method seems to be excellent in accuracy, robustness and efficiency compared to other ones when applied to crashworthiness optimization. Therefore the application of such meta-model was used in this work, in order to improve the structural optimization of a bumper for a racing car in composite material subjected to frontal impact. The specific energy absorption represents the objective function to maximize and the geometrical parameters subjected to some design constraints are the design variables. LS-DYNA codes were interfaced with LS-OPT tool in order to find the optimized solution, through the use of a domain reduction strategy. With the use of the Kriging meta-model the crashworthiness characteristic of the composite bumper was improved.

Keywords: Composite material, crashworthiness, finite element analysis, optimization.

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Authors: Mazyar Ahrari, Ramin Khajavi, Mehdi Kamali Dolatabadi, Tayebeh Toliyat, Abosaeed Rashidi

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Fabric as the first and most common layer that is in permanent contact with human skin is a very good interface to provide coverage, as well as heat and cold insulation. Phase change materials (PCMs) are organic and inorganic compounds which have the capability of absorbing and releasing noticeable amounts of latent heat during phase transitions between solid and liquid phases at a low temperature range. PCMs come across phase changes (liquid-solid and solid-liquid transitions) during absorbing and releasing thermal heat; so, in order to use them for a long time, they should have been encapsulated in polymeric shells, so-called microcapsules. Microencapsulation and nanoencapsulation methods have been developed in order to reduce the reactivity of a PCM with outside environment, promoting the ease of handling, decreasing the diffusion and evaporation rates. Methods of incorporation of PCMs in textiles such as electrospinning and determining thermal properties had been summarized. Paraffin waxes catch a lot of attention due to their high thermal storage density, repeatability of phase change, thermal stability, small volume change during phase transition, chemical stability, non-toxicity, non-flammability, non-corrosive and low cost and they seem to play a key role in confronting with climate change and global warming. In this article, we aimed to review the researches concentrating on the characteristics of PCMs and new materials and methods of microencapsulation.

Keywords: Thermoregulation, phase change materials, microencapsulation, thermal energy storage, nanoencapsulation.

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Authors: Okafor UAC, Ibeabuchimn, Omidina JO, Igwesi-Chidobe CN, Akinbo SRA

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Purpose: The fear of falling is a major concern among the elderly. Sixty-five percent of individuals older than 60 years of age experience loss of balance often on a daily basis. Therefore, balance assessment in the elderly deserves special attention due to its importance in functional mobility and safety. This study aimed at assessing balance performance and comparing some anthropometric parameters among a Nigerian non-institutionalized elderly population.

Methods: Sixty one elderly subjects (31 males and 30 females) participated in this study. Their ages ranged between 62 and 84 years. Ability to maintain balance was assessed using Functional Reach Test (FRT) and Sharpened Romberg Test (SRT). Anthropometric data including age, weight, height, arm length, leg length, bi-acromial breadth, foot length and trunk length were also collected. Analysis was done using Pearson’s Product Moment Correlation Coefficient and Independent T-test, while level of significance was set as p<0.05.

Results: Age-related significant relationship was observed between balance performance and bi-acromial breadth among the elderly population. Gender and visual input also had a significant influence on balance performance. Other anthropometric variables (age, weight, height, arm length, leg length, foot length and trunk length) showed no significant relationship with balance performance among this elderly sample.

Conclusion: Only specific anthropometric variables may affect balance performances among the healthy elderly. The study further highlights the need for routine assessment of both static and dynamic balance to detect and appropriately manage aging-related diseases which could affect balance in the elderly.

Keywords: Balance Performance, Anthropometry, Non-institutionalized Elderly.

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Authors: Enas Elsayed Abutaleb, Mohamed Taher Eldesoky, Shahenda Abd El Rasol

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Background: Muscle Energy Techniques (MET) have been widely used by manual therapists over the past years, but still limited research validated its use and there was limited evidence to substantiate the theories used to explain its effects. Objective: To investigate the effect of Muscle Energy Technique (MET) on anterior pelvic tilt in patients with lumbar spondylosis. Design: Randomized controlled trial. Subjects: Thirty patients with anterior pelvic tilt from both sexes were involved, aged between 35 to 50 years old and they were divided into MET and control groups with 15 patients in each. Methods: All patients received 3sessions/week for 4 weeks where the study group received MET, Ultrasound and Infrared, and the control group received U.S and I.R only. Pelvic angle was measured by palpation meter, pain severity by the visual analogue scale and functional disabilities by the Oswestry disability index. Results: Both groups showed significant improvement in all measured variables. The MET group was significantly better than the control group in pelvic angle, pain severity, and functional disability as p-value were (0.001, 0.0001, 0.0001) respectively. Conclusion and implication: the study group fulfilled greater improvement in all measured variables than the control group which implies that application of MET in combination with U.S and I.R were more effective in improving pelvic tilting angle, pain severity and functional disabilities than using electrotherapy only.

Keywords: Anterior pelvic tilt, lumbar spondylosis, muscle energy technique exercise, palpation meter.

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Authors: Muhammad Imran, Iqra Basit, Mobushir Riaz Khan, Sajid Rasheed Ahmad

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The present study investigates the space-time impact of climate change on the rice crop calendar in tropical Gujranwala, Pakistan. The climate change impact was quantified through the climatic variables, whereas the existing calendar of the rice crop was compared with the phonological stages of the crop, depicted through the time series of the Normalized Difference Vegetation Index (NDVI) derived from Landsat data for the decade 2005-2015. Local maxima were applied on the time series of NDVI to compute the rice phonological stages. Panel models with fixed and cross-section fixed effects were used to establish the relation between the climatic parameters and the time-series of NDVI across villages and across rice growing periods. Results show that the climatic parameters have significant impact on the rice crop calendar. Moreover, the fixed effect model is a significant improvement over cross-sectional fixed effect models (R-squared equal to 0.673 vs. 0.0338). We conclude that high inter-annual variability of climatic variables cause high variability of NDVI, and thus, a shift in the rice crop calendar. Moreover, inter-annual (temporal) variability of the rice crop calendar is high compared to the inter-village (spatial) variability. We suggest the local rice farmers to adapt this change in the rice crop calendar.

Keywords: Landsat NDVI, panel models, temperature, rainfall.

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Authors: Najm M. Al-Hosiny

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We theoretically investigate the effects of frequency detuning and injection power on the nonlinear dynamics of DFB lasers under dual external optical injection.

Keywords: Optical injection, DFB laser, frequency detuning, injection power.

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Authors: John Michael Russo

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Honeybees are important to our food system and continue to suffer from high rates of colony loss. Precision feed has brought many benefits to livestock cultivation and these should transfer to apiculture. However, apiculture has unique challenges. The objective of this research is to understand how principles of precision agriculture, applied to apiculture and feed specifically, might effectively improve state-of-the-art cultivation. The methodology surveys apicultural practice to build a model for assessment. First, a review of apicultural motivators is made. Feed method is then evaluated. Finally, precision feed methods are examined as accelerants with potential to advance the effectiveness of feed practice. Six important motivators emerge: colony loss, disease, climate change, site variance, operational costs, and competition. Feed practice itself is used to compensate for environmental variables. The research finds that the current state-of-the-art in apiculture feed focuses on critical challenges in the management of feed schedules which satisfy requirements of the bees, preserve potency, optimize environmental variables, and manage costs. Many of the challenges are most acute when feed is used to dispense medication. Technology such as RNA treatments have even more rigorous demands. Precision feed solutions focus on strategies which accommodate specific needs of individual livestock. A major component is data; they integrate precise data with methods that respond to individual needs. There is enormous opportunity for precision feed to improve apiculture through the integration of precision data with policies to translate data into optimized action in the apiary, particularly through automation.

Keywords: Apiculture, precision apiculture, RNA varroa treatment, honeybee feed applications.

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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: F. Rezaie Moghaddam, T. Rezaie Moghaddam, M. Pasbani Khiavi, M. Ali Ghorbani

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Identifying and classifying intersections according to severity is very important for implementation of safety related counter measures and effective models are needed to compare and assess the severity. Highway safety organizations have considered intersection safety among their priorities. In spite of significant advances in highways safety, the large numbers of crashes with high severities still occur in the highways. Investigation of influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Previous studies lacked a model capable of simultaneous illustration of the influence of human factors, road, vehicle, weather conditions and traffic features including traffic volume and flow speed on the crash severity. Thus, this paper is aimed at developing the models to illustrate the simultaneous influence of these variables on the crash severity in urban highways. The models represented in this study have been developed using binary Logit Models. SPSS software has been used to calibrate the models. It must be mentioned that backward regression method in SPSS was used to identify the significant variables in the model. Consider to obtained results it can be concluded that the main factor in increasing of crash severity in urban highways are driver age, movement with reverse gear, technical defect of the vehicle, vehicle collision with motorcycle and bicycle, bridge, frontal impact collisions, frontal-lateral collisions and multi-vehicle crashes in urban highways which always increase the crash severity in urban highways.

Keywords: Backward regression, crash severity, speed, urbanhighways.

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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: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: Aero-thermo-elasticity, elastic deformation, structural temperature, multi-field coupling.

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Authors: Fang Yao, Minglei Chen

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City shrinkage is one of the thorny problems that many European cities have to face with nowadays. It is mainly expressed as the decrease of population in these cities. Eastern Germany is one of the pioneers of European shrinking cities with long shrinking history. The paper selects one representative shrinking city Halle (Saale) in eastern Germany as research objective, collecting and investigating nearly 20 years (1993-2010) municipal data after the reunification of Germany. These data based on five dimensions, which are demographic, economic, social, spatial and environmental and total 16 eligible variables. Factor Analysis is used to deal with these variables in order to assess the most important factors affecting shrinking Halle. The results show that there are three main factors determine the shrinkage of Halle, respectively named “demographical and economical factor”, “social stability factor”, and “city vitality factor”. The three factors act at different time period of Halle’s shrinkage: from 1993 to 1997 the demographical and economical factor played an important role; from 1997 to 2004 the social stability factor is significant to city shrinkage; since 2005 city vitality factor determines the shrinkage of Halle. In recent years, the shrinkage in Halle mitigates that shows the sign of growing population. Thus the city Halle should focus on attaching more importance on the city vitality factor to prevent the city from shrinkage. Meanwhile, the city should possess a positive perspective to shift the growth-oriented development to tap the potential of shrinking cities. This method is expected to apply to further research and other shrinking cities

Keywords: Demography, Factor analysis, Halle, Shrinking cities.

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Authors: Stephen B. Schepman, Michael A. Zarate

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The purpose of this study was to explore the relationship between Burnout, Negative Affectivity, and Organizational Citizenship Behavior (OCB) for social service workers at two agencies serving homeless populations. Thirty two subjects completed surveys. Significant correlations between major variables and subscales were found.

Keywords: Burnout, negative affectivity, organizationalcitizenship.

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Authors: Redouane Tlemsani, Abdelkader Benyettou

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Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology.

This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data.

Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables.

In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization.

The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition.

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Authors: Muftah H. El-Naas, Ameera F. Mohammad, Mabruk I. Suleiman, Mohamed Al Musharfy, Ali H. Al-Marzouqi

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CO₂ capture and storage technologies play a significant role in contributing to the control of climate change through the reduction of carbon dioxide emissions into the atmosphere. The present study evaluates and optimizes CO₂ capture through a process, where carbon dioxide is passed into pH adjusted high salinity water and reacted with sodium chloride to form a precipitate of sodium bicarbonate. This process is based on a modified Solvay process with higher CO₂ capture efficiency, higher sodium removal, and higher pH level without the use of ammonia. The process was tested in a bubble column semi-batch reactor and was optimized using response surface methodology (RSM). CO₂ capture efficiency and sodium removal were optimized in terms of major operating parameters based on four levels and variables in Central Composite Design (CCD). The operating parameters were gas flow rate (0.5–1.5 L/min), reactor temperature (10 to 50 ^oC), buffer concentration (0.2-2.6%) and water salinity (25-197 g NaCl/L). The experimental data were fitted to a second-order polynomial using multiple regression and analyzed using analysis of variance (ANOVA). The optimum values of the selected variables were obtained using response optimizer. The optimum conditions were tested experimentally using desalination reject brine with salinity ranging from 65,000 to 75,000 mg/L. The CO₂ capture efficiency in 180 min was 99% and the maximum sodium removal was 35%. The experimental and predicted values were within 95% confidence interval, which demonstrates that the developed model can successfully predict the capture efficiency and sodium removal using the modified Solvay method.

Keywords: Bubble column reactor, CO2 capture, Response Surface Methodology, water desalination.

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Authors: Salah R. Al Zaidee, Ali S. Mahdi

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Except for simple problems of statically determinate structures, optimum design problems in structural engineering have implicit objective functions where structural analysis and design are essential within each searching loop. With these implicit functions, the structural engineer is usually enforced to write his/her own computer code for analysis, design, and searching for optimum design among many feasible candidates and cannot take advantage of available software for structural analysis, design, and searching for the optimum solution. The meta-model is a regression model used to transform an implicit objective function into objective one and leads in turn to decouple the structural analysis and design processes from the optimum searching process. With the meta-model, well-known software for structural analysis and design can be used in sequence with optimum searching software. In this paper, the meta-model has been used to develop an explicit objective function for plane steel frames subjected to dead, live, and seismic forces. Frame topology is assumed as predefined based on architectural and functional requirements. Columns and beams sections and different connections details are the main design variables in this study. Columns and beams are grouped to reduce the number of design variables and to make the problem similar to that adopted in engineering practice. Data for the implicit objective function have been generated based on analysis and assessment for many design proposals with CSI SAP software. These data have been used later in SPSS software to develop a pure quadratic nonlinear regression model for the explicit objective function. Good correlations with a coefficient, R², in the range from 0.88 to 0.99 have been noted between the original implicit functions and the corresponding explicit functions generated with meta-model.

Keywords: Meta-modal, objective function, steel frames, seismic analysis, design.

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Authors: Taekyoon Park, Seok Goo Lee, Sung Ho Kim, Ung Lee, Jong Min Lee, Chonghun Han

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CO₂ capture and storage/sequestration (CCS) is a key technology for addressing the global warming issue. This paper proposes an integrated model for the whole chain of CCS, from a power plant to a reservoir. The integrated model is further utilized to determine optimal operating conditions and study responses to various changes in input variables.

Keywords: CCS, Caron Dioxide, Carbon Capture and Storage, Simulation, Optimization.

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Authors: Manat Srivanit, Hokao Kazunori

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Facing the concern of the population to its environment and to climatic change, city planners are now considering the urban climate in their choices of planning. The urban climate, representing different urban morphologies across central Bangkok metropolitan area (BMA), are used to investigates the effects of both the composition and configuration of variables of urban morphology indicators on the summer diurnal range of urban climate, using correlation analyses and multiple linear regressions. Results show first indicate that approximately 92.6% of the variation in the average maximum daytime near-surface air temperature (Ta) was explained jointly by the two composition variables of urban morphology indicators including open space ratio (OSR) and floor area ratio (FAR). It has been possible to determine the membership of sample areas to the local climate zones (LCZs) using these urban morphology descriptors automatically computed with GIS and remote sensed data. Finally result found the temperature differences among zones of large separation, such as the city center could be respectively from 35.48±1.04ºC (Mean±S.D.) warmer than the outskirt of Bangkok on average for maximum daytime near surface temperature to 28.27±0.21ºC for extreme event and, can exceed as 8ºC. A spatially disaggregation of urban thermal responsiveness map would be helpful for several reasons. First, it would localize urban areas concerned by different climate behavior over summer daytime and be a good indicator of urban climate variability. Second, when overlaid with a land cover map, this map may contribute to identify possible urban management strategies to reduce heat wave effects in BMA.

Keywords: Urban climate, Urban morphology, Local climate zone, Urban planning, GIS and remote sensing

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Authors: John Hadden, Ashutosh Tiwari, Rajkumar Roy, Dymitr Ruta

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The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn.

Keywords: Churn, Decision Trees, Neural Networks, Regression.

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Authors: B. Nassar, W. Hussein, M. Mokhtar

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The critical concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission, but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the problem above coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions, and the results show that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.

Keywords: Space telemetry monitoring, multivariate analysis, PCA algorithm, space operations.

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Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

Abstract:

The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: Educative model, good life, professional social responsibility (SR), values.

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Authors: Ju-Chun Chien

Abstract:

The Ottawa Charter for Health Promotion proposed that the role of health services should shift the focus from cure to prevention. Nowadays, besides having physical examinations, people could also conduct genetic tests to provide important information for diagnosing, treating, and/or preventing illnesses. However, because of the incompletion of the Chinese Genetic Database, people in Taiwan were still unfamiliar with genetic testing. The purposes of the present study were to: (1) Figure out people’s attitudes towards genetic testing. (2) Examine factors that influence people’s intention to pursue genetic testing by means of the Health Belief Model (HBM). A pilot study was conducted on 249 Taiwanese in 2017 to test the feasibility of the self-developed instrument. The reliability and construct validity of scores on the self-developed questionnaire revealed that this HBM-based questionnaire with 40 items was a well-developed instrument. A total of 542 participants were recruited and the valid participants were 535 (99%) between the ages of 20 and 86. Descriptive statistics, one-way ANOVA, two-way contingency table analysis, Pearson’s correlation, and stepwise multiple regression analysis were used in this study. The main results were that only 32 participants (6%) had already undergone genetic testing; moreover, their attitude towards genetic testing was more positive than those who did not have the experience. Compared with people who never underwent genetic tests, those who had gone for genetic testing had higher self-efficacy, greater intention to pursue genetic testing, had academic majors in health-related fields, had chronic and genetic diseases, possessed Catastrophic Illness Cards, and all of them had heard about genetic testing. The variables that best predicted people’s intention to pursue genetic testing were cues to action, self-efficacy, and perceived benefits (the three variables all correlated with one another positively at high magnitudes). To sum up, the HBM could be effective in designing and identifying the needs and priorities of the target population to pursue genetic testing.

Keywords: Genetic testing, intention to pursue genetic testing, Taiwan, health belief model.

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Authors: Yilun Shang

Abstract:

Let {Xi}i≥1 be a martingale difference sequence with Xi = Si - Si-1. Under some regularity conditions, we show that (X2 1+· · ·+X2N n)-1/2SNn is asymptotically normal, where {Ni}i≥1 is a sequence of positive integer-valued random variables tending to infinity. In a similar manner, a backward (or reverse) martingale central limit theorem with random indices is provided.

Keywords: central limit theorem, martingale difference sequence, backward martingale.

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Authors: Bernardo C. P. Albuquerque, Darym J. F. Campos

Abstract:

Increasing our ability to solve complex engineering problems is directly related to the processing capacity of computers. By means of such equipments, one is able to fast and accurately run numerical algorithms. Besides the increasing interest in numerical simulations, probabilistic approaches are also of great importance. This way, statistical tools have shown their relevance to the modelling of practical engineering problems. In general, statistical approaches to such problems consider that the random variables involved follow a normal distribution. This assumption tends to provide incorrect results when skew data is present since normal distributions are symmetric about their means. Thus, in order to visualize and quantify this aspect, 9 statistical distributions (symmetric and skew) have been considered to model a hypothetical slope stability problem. The data modeled is the friction angle of a superficial soil in Brasilia, Brazil. Despite the apparent universality, the normal distribution did not qualify as the best fit. In the present effort, data obtained in consolidated-drained triaxial tests and saturated direct shear tests have been modeled and used to analytically derive the probability density function (PDF) of the safety factor of a hypothetical slope based on Mohr-Coulomb rupture criterion. Therefore, based on this analysis, it is possible to explicitly derive the failure probability considering the friction angle as a random variable. Furthermore, it is possible to compare the stability analysis when the friction angle is modelled as a Dagum distribution (distribution that presented the best fit to the histogram) and as a Normal distribution. This comparison leads to relevant differences when analyzed in light of the risk management.

Keywords: Statistical slope stability analysis, Skew distributions, Probability of failure, Functions of random variables.

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Authors: Himali N. Balasooriya, Kithsiri B. Dassanayake, Saman Seneweera, Said Ajlouni

Abstract:

Increase in atmospheric CO₂ concentration [CO₂] and ambient temperature associated with changing climatic conditions will have significant impacts on agriculture crop productivity and quality. Independent effects of the above two environmental variables on the growth, yield and quality of strawberry were well documented. Higher temperatures over the optimum range (20-25ºC) lead to crop failures, while elevated [CO₂] stimulated plant growth and yield but compromised the physical quality of fruits. However, there is very limited understanding of the interaction between these variables on the plant growth, yield and quality. Therefore, this study was designed to investigate the interactive effect of high temperature and elevated [CO₂] on growth, yield and quality of strawberries. Strawberry cultivars ‘Albion’ and ‘San Andreas’ were grown under six different combinations of two temperatures (25 and 30ºC) and three [CO₂] (400, 650 and 950 µmol mol^-1) in controlled-environmental growth chambers. Plant growth measurements such as plant height, canopy area, number of flowers, and fruit yield were measured during phonological development. Photosynthesis and transpiration, the ratio of intercellular to atmospheric [CO₂] (Ci/Ca) were measured to estimate the physiological adjustment to climate stress. The impact of temperature and [CO₂] interaction on growth and yield of strawberry was significant (p < 0.05). Across both cultivars, highest fruit yields were observed at 650 µmol mol^-1 [CO₂], which was particularly clear at 25°C. The fruit yield gradually decreased at 30°C under all the treatment combinations. However, photosynthesis rates were highest at 650 µmol mol^-1 [CO₂] but no increment was found at 900 µmol mol^-1 [CO₂]. Interestingly, Ci/Ca ratio increased with increasing atmospheric [CO₂] which was predominant at high temperature. Similarly, fruit yield was substantially reduced at high [CO₂] under high temperature. Our findings suggest that increased Ci/Ca ratio at high temperature is likely reduces the photosynthesis and thus yield response to elevated [CO₂].

Keywords: Atmospheric [CO2], fruit yield, strawberry, temperature.

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