Search results for: fuzzy regression

Authors: M. Capocelli, A. Caputo, M. De Falco, D. Mazzei, V. Piemonte

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This paper presents the experimental results and the related modeling of a thermal energy storage (TES) facility, ideated and realized by ENEA and realizing the thermocline with an innovative geometry. Firstly, the thermal energy exchange model of an equivalent shell & tube heat exchanger is described and tested to reproduce the performance of the spiral exchanger installed in the TES. Through the regression of the experimental data, a first-order thermocline model was also validated to provide an analytical function of the thermocline, useful for the performance evaluation and the comparison with other systems and implementation in simulations of integrated systems (e.g. power plants). The experimental data obtained from the plant start-up and the short-cut modeling of the system can be useful for the process analysis, for the scale-up of the thermal storage system and to investigate the feasibility of its implementation in actual case-studies.

Keywords: Thermocline, modelling, heat exchange, spiral, shell, tube.

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: Client classification, loan suitability, risk rating, CART analysis, decision tree.

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Authors: Monika Jain, Shikhar Puri, Shivali Unishree

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This paper presents the ‘Eye Ball Motion Controlled Wheelchair using IR Sensors’ for the elderly and differently abled people. In this eye tracking based technology, three Proximity Infrared (IR) sensor modules are mounted on an eye frame to trace the movement of the iris. Since, IR sensors detect only white objects; a unique sequence of digital bits is generated corresponding to each eye movement. These signals are then processed via a micro controller IC (PIC18F452) to control the motors of the wheelchair. The potential and efficiency of previously developed rehabilitation systems that use head motion, chin control, sip-n-puff control, voice recognition, and EEG signals variedly have also been explored in detail. They were found to be inconvenient as they served either limited usability or non-affordability. After multiple regression analyses, the proposed design was developed as a cost-effective, flexible and stream-lined alternative for people who have trouble adopting conventional assistive technologies.

Keywords: Eye tracking technology, Intelligent wheelchair, IR module, rehabilitation technology.

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, Prediction modeling, rail track degradation.

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Authors: Rouzbeh Ramezani, Renzo Di Felice

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Nowadays, removal of CO₂ as one of the major contributors to global warming using alternative solvents with high CO₂ absorption efficiency, is an important industrial operation. In this study, three amines, including 2-methylpiperazine, potassium sarcosinate and potassium lysinate as potential additives, were added to the potassium carbonate solution as a base solvent for CO₂ capture. In order to study the absorption performance of CO₂ in terms of loading capacity of CO₂ and absorption rate, the absorption experiments in a blend of additives with potassium carbonate were carried out using the vapor-liquid equilibrium apparatus at a temperature of 313.15 K, CO₂ partial pressures ranging from 0 to 50 kPa and at mole fractions 0.2, 0.3, and 0.4. Furthermore, the performance of CO₂ absorption in these blend solutions was compared with pure monoethanolamine and with pure potassium carbonate. Finally, a correlation with good accuracy was developed using the nonlinear regression analysis in order to predict CO₂ loading capacity.

Keywords: Absorption rate, carbon dioxide, CO2 capture, global warming, loading capacity.

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Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

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Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: Growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises.

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Authors: Deniz Deryakulu, Şener Büyüköztürk Hüseyin Özçınar

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The main purpose of this study was to determine the predictors of academic achievement of student Information and Communications Technologies (ICT) teachers with different learning styles. Participants were 148 student ICT teachers from Ankara University. Participants were asked to fill out a personal information sheet, the Turkish version of Kolb-s Learning Style Inventory, Weinstein-s Learning and Study Strategies Inventory, Schommer's Epistemological Beliefs Questionnaire, and Eysenck-s Personality Questionnaire. Stepwise regression analyses showed that the statistically significant predictors of the academic achievement of the accommodators were attitudes and high school GPAs; of the divergers was anxiety; of the convergers were gender, epistemological beliefs, and motivation; and of the assimilators were gender, personality, and test strategies. Implications for ICT teaching-learning processes and teacher education are discussed.

Keywords: Academic achievement, student ICT teachers, Kolb learning styles, experiential learning.

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Authors: Fengxia Zheng, Shouming Zhong

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ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.

Keywords: Binomial smoothing (BS), hybrid, Canadian Lynx data, forecasting accuracy.

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Authors: K. Petcharaporn, S. Kumchoo

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The acidity (citric acid) is the one of chemical content that can be refer to the internal quality and it’s a maturity index of tomato, The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR) spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomato.

Keywords: Tomato, quality, prediction, transmittance, titratable acidity, citric acid.

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Authors: Rose Shayeghi, Pejman Hosseinioun

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The Multiple Intelligences theory characterizes human intelligence as a multifaceted entity that exists in all human beings with varying degrees. The most important contribution of this theory to the field of English Language Teaching (ELT) is its role in identifying individual differences and designing more learnercentered programs. The present study aims at investigating the relationship between different elements of multiple intelligence and grammar scores. To this end, 63 female Iranian EFL learner selected from among intermediate students participated in the study. The instruments employed were a Nelson English language test, Michigan Grammar Test, and Teele Inventory for Multiple Intelligences (TIMI). The results of Pearson Product-Moment Correlation revealed a significant positive correlation between grammatical accuracy and linguistic as well as interpersonal intelligence. The results of Stepwise Multiple Regression indicated that linguistic intelligence contributed to the prediction of grammatical accuracy.

Keywords: Multiple intelligence, grammar, ELT, EFL, TIMI.

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Authors: Shilpi Tyagi, D. K. Nauriyal

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This study investigates the firm level determinants of profitability of Indian drug and pharmaceutical industry. The study uses inflation adjusted panel data for a period 2000-2013 and applies OLS regression model with Driscoll-Kraay standard errors. It has been found that export intensity, A&M intensity, firm’s market power and stronger patent regime dummy have exercised positive influence on profitability. The negative and statistically significant influence of R&D intensity and raw material import intensity points to the need for firms to adopt suitable investment strategies. The study suggests that firms are required to pay far more attention to optimize their operating expenditures, advertisement and marketing expenditures and improve their export orientation, as part of the long term strategy.

Keywords: Indian drug and pharmaceutical industry, trade related intellectual property rights, research and development, food and drug administration.

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Authors: Md. Z. Alam, Devi R. Asih, Md. N. Salleh

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Immobilization of lipase enzyme produced from palm oil mill effluent (POME) by the activated carbon (AC) among the low cost support materials was optimized. The results indicated that immobilization of 94% was achieved by AC as the most suitable support material. A sequential optimization strategy based on a statistical experimental design, including one-factor-at-a-time (OFAT) method was used to determine the equilibrium time. Three components influencing lipase immobilization were optimized by the response surface methodology (RSM) based on the face-centered central composite design (FCCCD). On the statistical analysis of the results, the optimum enzyme concentration loading, agitation rate and carbon active dosage were found to be 30 U/ml, 300 rpm and 8 g/L respectively, with a maximum immobilization activity of 3732.9 U/g-AC after 2 hrs of immobilization. Analysis of variance (ANOVA) showed a high regression coefficient (R²) of 0.999, which indicated a satisfactory fit of the model with the experimental data. The parameters were statistically significant at p<0.05.

Keywords: Activated carbon, adsorption, immobilization, POME based lipase.

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Authors: Yogesh Aggarwal

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The paper discusses the results obtained to predict reinforcement in singly reinforced beam using Neural Net (NN), Support Vector Machines (SVM-s) and Tree Based Models. Major advantage of SVM-s over NN is of minimizing a bound on the generalization error of model rather than minimizing a bound on mean square error over the data set as done in NN. Tree Based approach divides the problem into a small number of sub problems to reach at a conclusion. Number of data was created for different parameters of beam to calculate the reinforcement using limit state method for creation of models and validation. The results from this study suggest a remarkably good performance of tree based and SVM-s models. Further, this study found that these two techniques work well and even better than Neural Network methods. A comparison of predicted values with actual values suggests a very good correlation coefficient with all four techniques.

Keywords: Linear Regression, M5 Model Tree, Neural Network, Support Vector Machines.

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Authors: Nafisur Rahman, Habibur Rahman, Syed Najmul Hejaz Azmi

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This paper presents a simple and sensitive kinetic spectrophotometric method for the determination of ramipril in commercial dosage forms. The method is based on the reaction of the drug with 1-chloro-2,4-dinitrobenzene (CDNB) in dimethylsulfoxide (DMSO) at 100 ± 1ºC. The reaction is followed spectrophotometrically by measuring the rate of change of the absorbance at 420 nm. Fixed-time (ΔA) and equilibrium methods are adopted for constructing the calibration curves. Both the calibration curves were found to be linear over the concentration ranges 20 - 220 μg/ml. The regression analysis of calibration data yielded the linear equations: Δ A = 6.30 × 10-4 + 1.54 × 10-3 C and A = 3.62 × 10-4 + 6.35 × 10-3 C for fixed time (Δ A) and equilibrium methods, respectively. The limits of detection (LOD) for fixed time and equilibrium methods are 1.47 and 1.05 μg/ml, respectively. The method has been successfully applied to the determination of ramipril in commercial dosage forms. Statistical comparison of the results shows that there is no significant difference between the proposed methods and Abdellatef-s spectrophotometric method.

Keywords: Equilibrium method, Fixed-time (ΔA) method, Ramipril, Spectrophotometry.

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Authors: Aiyeloja A.A, Ogunjinmi A.A

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The recurring decimal of rural and urban poverty in Nigeria, resulting from lack of sustainable livelihood activities by the people due to non-diversification of the economy, necessitated this study. One hundred snail farmers were randomly selected in Akure North and Akure South Local Government areas of Ondo State, Southwest Nigeria where snail farming is widely practised. Data collection was through questionnaires administration and onsite observation of farms. Data obtained were subjected to descriptive statistics, Student-s t-test and regression analysis. Cost benefit ratio (CBR) and rate of return on investment (RORI) were calculated in order to determine the poverty alleviation potentials of snail farming in the study areas. Although snail farming was profitable and viable, it was below poverty line. With time and more knowledge in its farming activities, and with more people taking to snail production, its poverty alleviation and reduction potentials will increase.

Keywords: Alleviation, farming, Nigeria, potential, poverty, snail.

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Authors: Farzin Salmasi

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Energy dissipation in drops has been investigated by physical models. After determination of effective parameters on the phenomenon, three drops with different heights have been constructed from Plexiglas. They have been installed in two existing flumes in the hydraulic laboratory. Several runs of physical models have been undertaken to measured required parameters for determination of the energy dissipation. Results showed that the energy dissipation in drops depend on the drop height and discharge. Predicted relative energy dissipations varied from 10.0% to 94.3%. This work has also indicated that the energy loss at drop is mainly due to the mixing of the jet with the pool behind the jet that causes air bubble entrainment in the flow. Statistical model has been developed to predict the energy dissipation in vertical drops denotes nonlinear correlation between effective parameters. Further an artificial neural networks (ANNs) approach was used in this paper to develop an explicit procedure for calculating energy loss at drops using NeuroSolutions. Trained network was able to predict the response with R2 and RMSE 0.977 and 0.0085 respectively. The performance of ANN was found effective when compared to regression equations in predicting the energy loss.

Keywords: Air bubble, drop, energy loss, hydraulic jump, NeuroSolutions

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Authors: Mohammad Ali Lotfollahi-Yaghin, Hamid Ahmadi

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In the present paper, a set of parametric FE stress analyses is carried out for two-planar welded tubular DKT-joints under two different axial load cases. Analysis results are used to present general remarks on the effect of geometrical parameters on the stress concentration factors (SCFs) at the inner saddle, outer saddle, toe, and heel positions on the main (outer) brace. Then a new set of SCF parametric equations is developed through nonlinear regression analysis for the fatigue design of two-planar DKT-joints. An assessment study of these equations is conducted against the experimental data; and the satisfaction of the criteria regarding the acceptance of parametric equations is checked. Significant effort has been devoted by researchers to the study of SCFs in various uniplanar tubular connections. Nevertheless, for multi-planar joints covering the majority of practical applications, very few investigations have been reported due to the complexity and high cost involved.

Keywords: Offshore jacket structure, Parametric equation, Stress concentration factor (SCF), Two-planar tubular KT-joint

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Authors: M. S. Gaafar, S. Y. Marzouk, I. S. Mahmoud, S. Al-Zobaidi

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Makishima and Mackenzie model was used to simulation of acoustic properties (longitudinal and shear ultrasonic wave velocities, elastic moduli theoretically for many tellurite and borate glasses. The model was proposed mainly depending on the values of the experimentally measured density, which are obtained before. In this search work, we are trying to obtain the values of densities of amorphous glasses (as the density depends on the geometry of the network structure of these glasses). In addition, the problem of simulating the slope of linear regression between the experimentally determined bulk modulus and the product of packing density and experimental Young's modulus, were solved in this search work. The results showed good agreement between the experimentally measured values of densities and both ultrasonic wave velocities, and those theoretically determined.

Keywords: Glasses, ultrasonic wave velocities, elastic moduli, Makishima and Mackenzie model.

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Authors: Bostjan Antoncic

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Technological newness and innovativeness are important aspects of small firm development, growth and wealth creation. The contribution of the study to entrepreneurship personality research and to technology-related research in entrepreneurship is that the model of the general personality driven technological development was developed and empirically tested. Hypotheses relating the big five personality factors (OCEAN: openness, conscientiousness, extraversion, agreeableness, and neuroticism) and technological developments were tested by using multiple regression analysis on survey data from a sample of 160 entrepreneurs from Slovenia. The model reveals two personality factors, which are predictive of technological developments: openness (positive impact) and neuroticism (negative impact). In addition, a positive impact of firm age on technological developments was found. Other personality factors (conscientiousness, extraversion and agreeableness) of entrepreneurs may not be considered important for their firm technological developments.

Keywords: Big five factors, entrepreneur, personality, technology development.

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Authors: Daranee Pimchangthong, Supaporn Tinprapa

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The objectives of this research were to explore factors influencing knowledge management process in the manufacturing industry and develop a model to support knowledge management processes. The studied factors were technology infrastructure, human resource, knowledge sharing, and the culture of the organization. The knowledge management processes included discovery, capture, sharing, and application. Data were collected through questionnaires and analyzed using multiple linear regression and multiple correlation. The results found that technology infrastructure, human resource, knowledge sharing, and culture of the organization influenced the discovery and capture processes. However, knowledge sharing had no influence in sharing and application processes. A model to support knowledge management processes was developed, which indicated that sharing knowledge needed further improvement in the organization.

Keywords: knowledge management, knowledge management process, tacit knowledge

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Authors: Mahmoud M. Tash

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The present study was carried out to investigate the effect of alloying elements and thermo-mechanical treatment (TMT) i.e. hot rolling and forging with different reduction ratios on the hardness (HV) and impact toughness (J) of heat-treated low alloy steels. An understanding of the combined effect of TMT and alloying elements and by measuring hardness, impact toughness, resulting from different heat treatment following TMT of the low alloy steels, it is possible to determine which conditions yielded optimum mechanical properties and high strength to weight ratio. Experimental Correlations between hot work reduction ratio, hardness and impact toughness for thermo-mechanically heat treated low alloy steels are analyzed quantitatively, and both regression and mathematical hardness and impact toughness models are developed.

Keywords: Hot Forging, hot rolling, heat treatment, hardness (hv), impact toughness (j), microstructure, low alloy steels.

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Authors: B. Engel, M. Junge

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Manufacturing components of fiber-reinforced thermoplastics requires three steps: heating the matrix, forming and consolidation of the composite and terminal cooling the matrix. For the heating process a pre-determined temperature distribution through the layers and the thickness of the pre-consolidated sheets is recommended to enable forming mechanism. Thus, a design for the heating process for forming composites with thermoplastic matrices is necessary. To obtain a constant temperature through thickness and width of the sheet, the heating process was analyzed by the help of the finite element method. The simulation models were validated by experiments with resistance thermometers as well as with an infrared camera. Based on the finite element simulation, heating methods for infrared radiators have been developed. Using the numeric simulation many iteration loops are required to determine the process parameters. Hence, the initiation of a model for calculating relevant process parameters started applying regression functions.

Keywords: Fiber-reinforced thermoplastics, heating strategies, middle-wave infrared radiator.

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Authors: Sabina Akter, Osiris Valdez Banda, Pentti Kujala, Jani Romanoff

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This paper examines the relationship between on-board environmental factors and customer overall satisfaction in the context of the cruise on-board experience. The on-board environmental factors considered are ambient, layout/design, social, product/service and on-board enjoyment factors. The study presents a data-driven framework and model for the on-board cruise experience. The data are collected from 893 respondents in an application of a self-administered online questionnaire of their cruise experience. This study reveals the cruise passengers’ on-board experience through the customer decision journey based on the publicly available data. Pearson correlation and regression analysis have been applied, and the results show a positive and a significant relationship between the environmental factors and on-board experience. These data help understand the cruise passengers’ on-board experience, which will be used for the ultimate decision-making process in cruise ship design.

Keywords: Cruise behavior, on-board environmental factors, on-board experience, user or customer satisfaction.

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Authors: Petr Květon, Martin Jelínek

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We try to identify the role of various aspects of parenting style in the phenomenon of videogame playing addiction. Relevant self-report questionnaires were part of a wider set of methods focused on the constructs related to videogame playing. The battery of methods was administered in school settings in paper and pencil form. The research sample consisted of 333 (166 males, 167 females) elementary and high school students at the age between 10 and 19 years (m=14.98, sd=1.77). Using stepwise regression analysis, we assessed the influence of demographic variables (gender and age) and parenting styles. Age and gender together explained 26.3% of game addiction variance (F(2,330)=58.81, p<.01). By adding four aspect of parenting styles (inconsistency, involvement, control, and warmth) another 10.2% of variance was explained (∆F(4,326)=13.09, p<.01). The significant predictor was gender of the respondent, where males scored higher on game addiction scale (B=0.70, p<.01), age (β=-0.18, p<.01), where younger children showed higher level of addiction, and parental inconsistency (β=0.30, p<.01), where the higher the inconsistency in upbringing, the more developed game playing addiction.

Keywords: Gender, parenting styles, video games, addiction.

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Authors: Wanida Suwunniponth

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The purpose of this research was to study the factors of characteristic of business, website quality and trust affected intention to use electronic payment systems for online purchasing. This survey research used questionnaire as a tool to collect the data of 300 customers who purchased online products and used an electronic payment system. The descriptive statistics and multiple regression analysis were used to analyze data. The results revealed that customers had a good opinion towards the characteristic of the business and website quality. However, they have a moderate opinion towards trust and intention to repurchase. In addition, the characteristics of the business affected the purchase intention the most, followed by website quality and the trust with statistical significance at 0.05 level. For particular, the terms of reputation, communication, information quality, perceived risk and word of mouth affected the intention to use the electronic payment system. In contrast, the terms of size, system quality and service quality did not affect intention to use an electronic payment system.

Keywords: Electronic payment, intention, online purchasing, trust.

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Authors: Popoola, C. F., Akinsanya, K., Babarinde, S. B., Farinde, D. A.

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This study investigated published financial statement as correlate of investment decision among commercial bank stakeholders in Nigeria. A correlation research design was used in the study. 180 users of published financial statement were purposively sampled from Lagos and Ibadan. Data generated were analyzed using Pearson correlation and regression. The findings of the study revealed that, balance sheet is negatively related with investment decision (r= -.483; p<.01) while income statement (r= .249; p<.001), notes on the account (r= .230; p<.001), cash flow statement (r= .202; p<.001), value added statement (r= .328; p<.001) and five-year financial summary (r= .191; p<.01) are positively related with investment decision. Findings also revealed that components of published financial statement significantly predicted good investment decision (R²= .983; F_(5,175)=284.5; p<.05) for commercial bank stakeholders. Therefore, it was suggested that Nigeria banks and professional bodies should instigate programs that will increase the knowledge of stakeholders on published financial statement.

Keywords: Commercial banks, Financial statement, Income Statement, Investment decision, Stakeholders.

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Authors: D Sudheer Reddy, N Gopal Reddy, P V Radhadevi, J Saibaba, Geeta Varadan

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Smoothing or filtering of data is first preprocessing step for noise suppression in many applications involving data analysis. Moving average is the most popular method of smoothing the data, generalization of this led to the development of Savitzky-Golay filter. Many window smoothing methods were developed by convolving the data with different window functions for different applications; most widely used window functions are Gaussian or Kaiser. Function approximation of the data by polynomial regression or Fourier expansion or wavelet expansion also gives a smoothed data. Wavelets also smooth the data to great extent by thresholding the wavelet coefficients. Almost all smoothing methods destroys the peaks and flatten them when the support of the window is increased. In certain applications it is desirable to retain peaks while smoothing the data as much as possible. In this paper we present a methodology called as peak-wise smoothing that will smooth the data to any desired level without losing the major peak features.

Keywords: smoothing, moving average, peakwise smoothing, spatialdensity models, planar shape models, wavelets.

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Authors: S. Loumi, H. Merrad, F. Alilat, B. Sansal

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In this article, we propose a methodology for the characterization of the suspended matter along Algiers-s bay. An approach by multi layers perceptron (MLP) with training by back propagation of the gradient optimized by the algorithm of Levenberg Marquardt (LM) is used. The accent was put on the choice of the components of the base of training where a comparative study made for four methods: Random and three alternatives of classification by K-Means. The samples are taken from suspended matter image, obtained by analytical model based on polynomial regression by taking account of in situ measurements. The mask which selects the zone of interest (water in our case) was carried out by using a multi spectral classification by ISODATA algorithm. To improve the result of classification, a cleaning of this mask was carried out using the tools of mathematical morphology. The results of this study presented in the forms of curves, tables and of images show the founded good of our methodology.

Keywords: Classification K-means, mathematical morphology, neural network MLP, remote sensing, suspended particulate matter

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Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon

Abstract:

This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: Human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence.

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Authors: Y. Parvizi, M. Gorji, M.H. Mahdian, M. Omid

Abstract:

Soil organic carbon (SOC) plays a key role in soil fertility, hydrology, contaminants control and acts as a sink or source of terrestrial carbon content that can affect the concentration of atmospheric CO2. SOC supports the sustainability and quality of ecosystems, especially in semi-arid region. This study was conducted to determine relative importance of 13 different exploratory climatic, soil and geometric factors on the SOC contents in one of the semiarid watershed zones in Iran. Two methods canonical discriminate analysis (CDA) and feed-forward back propagation neural networks were used to predict SOC. Stepwise regression and sensitivity analysis were performed to identify relative importance of exploratory variables. Results from sensitivity analysis showed that 7-2-1 neural networks and 5 inputs in CDA models output have highest predictive ability that explains %70 and %65 of SOC variability. Since neural network models outperformed CDA model, it should be preferred for estimating SOC.

Keywords: Soil organic carbon, modeling, neural networks, CDA.

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