Search results for: regression rate

Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

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In urban area, several landmarks may affect housing price and rents, hedonic analysis should employ distance variables corresponding to each landmarks. Unfortunately, the effects of distances to landmarks on housing prices are generally not consistent with the true price. These distance variables may cause magnitude error in regression, pointing a problem of spatial multicollinearity. In this paper, we provided some approaches for getting the samples with less bias and method on locating the specific sampling area to avoid the multicollinerity problem in two specific landmarks case.

Keywords: landmarks, hedonic regression, distance variables, collinearity, multicollinerity

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

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Authors: Orkide Donma, Mustafa M. Donma

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Basal metabolic rate is questioned as a risk factor for weight gain. The relations between basal metabolic rate and body composition have not been cleared yet. The impact of fat mass on basal metabolic rate is also uncertain. Within this context, indices based upon total body mass as well as total body fat mass are available. In this study, the aim is to investigate the potential clinical utility of these indices in the adult population. 287 individuals, aged from 18 to 79 years, were included into the scope of the study. Based upon body mass index values, 10 underweight, 88 normal, 88 overweight, 81 obese, and 20 morbid obese individuals participated. Anthropometric measurements including height (m), and weight (kg) were performed. Body mass index, diagnostic obesity notation model assessment index I, diagnostic obesity notation model assessment index II, basal metabolic rate-to-weight ratio were calculated. Total body fat mass (kg), fat percent (%), basal metabolic rate, metabolic age, visceral adiposity, fat mass of upper as well as lower extremities and trunk, obesity degree were measured by TANITA body composition monitor using bioelectrical impedance analysis technology. Statistical evaluations were performed by statistical package (SPSS) for Windows Version 16.0. Scatterplots of individual measurements for the parameters concerning correlations were drawn. Linear regression lines were displayed. The statistical significance degree was accepted as p < 0.05. The strong correlations between body mass index and diagnostic obesity notation model assessment index I as well as diagnostic obesity notation model assessment index II were obtained (p < 0.001). A much stronger correlation was detected between basal metabolic rate and diagnostic obesity notation model assessment index I in comparison with that calculated for basal metabolic rate and body mass index (p < 0.001). Upon consideration of the associations between basal metabolic rate-to-weight ratio and these three indices, the best association was observed between basal metabolic rate-to-weight and diagnostic obesity notation model assessment index II. In a similar manner, this index was highly correlated with fat percent (p < 0.001). Being independent of the indices, a strong correlation was found between fat percent and basal metabolic rate-to-weight ratio (p < 0.001). Visceral adiposity was much strongly correlated with metabolic age when compared to that with chronological age (p < 0.001). In conclusion, all three indices were associated with metabolic age, but not with chronological age. Diagnostic obesity notation model assessment index II values were highly correlated with body mass index values throughout all ranges starting with underweight going towards morbid obesity. This index is the best in terms of its association with basal metabolic rate-to-weight ratio, which can be interpreted as basal metabolic rate unit.

Keywords: basal metabolic rate, body mass index, children, diagnostic obesity notation model assessment index, obesity

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Authors: Rajeshwar Singh

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The presence of multicollinearity is common in handling with several explanatory variables simultaneously due to exhibiting a linear relationship among them. A great problem arises in understanding the impact of explanatory variables on the dependent variable. Thus, the method of least squares estimation gives inexact estimates. In this case, it is advised to detect its presence first before proceeding further. Using the ridge regression degree of its occurrence is reduced but principal components regression gives good estimates in this situation. This paper discusses well-known techniques of the ridge and principal components regressions and applies to get the estimates of coefficients by both techniques. In addition to it, this paper also discusses the conflicting claim on the discovery of the method of ridge regression based on available documents.

Keywords: conflicting claim on credit of discovery of ridge regression, multicollinearity, principal components and ridge regressions, variance inflation factor

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Authors: A. Bekbaev, M. Dzhamanbaev, R. Abitaeva, A. Karbozova, G. Nabyeva

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In this paper, the regression dependence of dancing intensity from wind speed and length of span was established due to the statistic data obtained from multi-year observations on line wires dancing accumulated by power systems of Kazakhstan and the Russian Federation. The lower and upper limitations of the equations parameters were estimated, as well as the adequacy of the regression model. The constructed model will be used in research of dancing phenomena for the development of methods and means of protection against dancing and for zoning plan of the territories of line wire dancing.

Keywords: power lines, line wire dancing, dancing intensity, regression equation, dancing area intensity

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Authors: Fangzheng Li, Xiong Li

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Significant differences in urban greening have occurred in Chinese cities, which accompanied with China's rapid urbanization. However, few studies focused on the spatial differentiation of urban greening in China with large amounts of data. The spatial differentiation pattern, spatial correlation characteristics and the distribution shape of urban green space ratio, urban green coverage rate and public green area per capita were calculated and analyzed, using Global and Local Moran's I using data from 289 cities in 2014. We employed Spatial Lag Model and Spatial Error Model to assess the impacts of urbanization process on urban greening of China. Then we used Geographically Weighted Regression to estimate the spatial variations of the impacts. The results showed: 1. a significant spatial dependence and heterogeneity existed in urban greening values, and the differentiation patterns were featured by the administrative grade and the spatial agglomeration simultaneously; 2. it revealed that urbanization has a negative correlation with urban greening in Chinese cities. Among the indices, the the proportion of secondary industry, urbanization rate, population and the scale of urban land use has significant negative correlation with the urban greening of China. Automobile density and per capita Gross Domestic Product has no significant impact. The results of GWR modeling showed that the relationship between urbanization and urban greening was not constant in space. Further, the local parameter estimates suggested significant spatial variation in the impacts of various urbanization factors on urban greening.

Keywords: China’s urbanization, geographically weighted regression, spatial differentiation pattern, urban greening

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

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In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: Ahmed Arkoazi, Hussein Znad, Ranjeet Utikar

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The synergistic impact and optimization of gas flow rate, concentration of CO₂, and light intensity on CO₂ biofixation rate were investigated using wastewater as a medium to cultivate Chlorella vulgaris under different conditions (gas flow rate 1-8 L/min), CO₂ concentration (0.03-7%), and light intensity (150-400 µmol/m².s)). Response Surface Methodology and Box-Behnken experimental Design were applied to find optimum values for gas flow rate, CO₂ concentration, and light intensity. The optimum values of the three independent variables (gas flow rate, concentration of CO₂, and light intensity) and desirability were 7.5 L/min, 3.5%, and 400 µmol/m².s, and 0.904, respectively. The highest amount of biomass produced and CO₂ biofixation rate at optimum conditions were 5.7 g/L, 1.23 gL^-1d^-1, respectively. The synergistic effect between gas flow rate and concentration of CO₂, and between gas flow rate and light intensity was significant on the three responses, while the effect between CO₂ concentration and light intensity was less significant on CO₂ biofixation rate. The results of this study could be highly helpful when using microalgae for CO₂ biofixation in wastewater treatment.

Keywords: bubble column reactor, gas holdup, hydrodynamics, sparger

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Authors: Nelson Bii, Christopher Ouma, John Odhiambo

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Non-response is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random non-response using auxiliary data. In this study, it is assumed that random non-response occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random non-response. In particular, the auxiliary information is used via an improved Nadaraya-Watson kernel regression technique to compensate for random non-response. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at a 95% coverage rate. The results obtained in this study are useful, for instance, in choosing efficient estimators of the finite population mean in demographic sample surveys.

Keywords: mean squared error, random non-response, two-stage cluster sampling, confidence interval lengths

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Authors: Jonardan Koner

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The study attempts to find out the impact of infrastructural investment on state economic growth in India. It further tries to determine the magnitude of the impact of infrastructural investment on economic indicator, i.e., per-capita income (PCI) in Indian States. The study uses panel regression technique to measure the impact of infrastructural investment on per-capita income (PCI) in Indian States. Panel regression technique helps incorporate both the cross-section and time-series aspects of the dataset. In order to analyze the difference in impact of the explanatory variables on the explained variables across states, the study uses Fixed Effect Panel Regression Model. The conclusions of the study are that infrastructural investment has a desirable impact on economic development and that the impact is different for different states in India. We analyze time series data (annual frequency) ranging from 1991 to 2010. The study reveals that the infrastructural investment significantly explains the variation of economic indicators.

Keywords: infrastructural investment, multiple regression, panel regression techniques, economic development, fixed effect dummy variable model

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Authors: R. Henry, H. Andriamboavonjy, J. B. Paulin, S. Drahy, R. Gourichon

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During past decade, Chinese monetary policy has been to maintain stability of exchange rate CNY/USD by creating parity between the two currencies. This policy, against market equilibrium, impacts the exchange rate in having low Yuan currency, and keeping attractiveness of Chinese industries. Using macroeconomic and statistic approach, the impact of such policy onto CNY/USD exchange rate is quantitatively determined. It is also pointed out how Chinese banks respect Basel III ratios, in particular the foreign exchange ratio. The main analysis is focusing on how Chinese banks will respect these ratios in the future.

Keywords: macroeconomics models, yuan floating exchange rate, basel iii, china banking system

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Authors: Mohamed Chabi, Mahmoud I. Syam, Sarah Aw

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In Summer 2012, the Foundation Program Unit of Qatar University has started implementing new ways of teaching Math by introducing MML (MyMathLab) as an innovative interactive tool to support standard teaching. In this paper, we focused on the effect of proper use of the Study Plan component of MML on student’s performance. Authors investigated the results of students of pre-calculus course during Fall 2013 in Foundation Program at Qatar University. The results showed that there is a strong correlation between study plan results and final exam results, also a strong relation between homework results and final exam results. In addition, the attendance average affected on the student’s results in general. Multiple regression is determined between passing rate dependent variable and study plan, homework as independent variable.

Keywords: MyMathLab, study plan, assessment, homework, attendance, correlation, regression

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Authors: I. Gokasar, G. Bakioglu

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With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: traffic app, real–time information, traffic congestion, regression analysis, dummy variables

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Authors: Daniel Fundi Murithi

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Missing data is a real bane in many surveys. To overcome the problems caused by missing data, partial deletion, and single imputation methods, among others, have been proposed. However, problems such as discarding usable data and inaccuracy in reproducing known population parameters and standard errors are associated with them. For regression and stochastic imputation, it is assumed that there is a variable with complete cases to be used as a predictor in estimating missing values in the other variable, and the relationship between the two variables is linear, which might not be realistic in practice. In this project, we estimate population total in presence of missing values in two-phase sampling. Instead of regression or stochastic models, non-parametric model based regression model is used in imputing missing values. Empirical study showed that nonparametric model-based regression imputation is better in reproducing variance of population total estimate obtained when there were no missing values compared to mean, median, regression, and stochastic imputation methods. Although regression and stochastic imputation were better than nonparametric model-based imputation in reproducing population total estimates obtained when there were no missing values in one of the sample sizes considered, nonparametric model-based imputation may be used when the relationship between outcome and predictor variables is not linear.

Keywords: finite population total, missing data, model-based imputation, two-phase sampling

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Authors: Nomakhosi Tshuma, Carley Chetty

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This study employed panel regression analysis to investigate the financial performance determinants of MTN and Vodacom’s rest of Africa businesses between 2012 to 2020. It used net profit margin, return on assets (ROA), and return on equity (ROE) as financial performance proxies. Financial performance determinants investigated were asset size, debt ratio, liquidity, number of subscribers, and exchange rate. Data relating to exchange rates were obtained from the World Bank website, while financial data and subscriber information were obtained from the companies’ audited financial statements. The study found statistically significant negative relationships between debt and both ROA and net profit, exchange rate and both ROA and net profit, and subscribers and ROE. It also found significant positive relationships between ROE and both asset size and exchange rate. The study recommends strategic options that optimise on the above findings, and these include infrastructure sharing to reduce infrastructure costs and the minimisation of foreign-denominated debt.

Keywords: financial performance, determinants of financial performance, business in Africa, telecommunications industry

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Authors: Dongjoo Kim, Chang-Sik Son, Won-Seok Kang

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Adequate sleep affects various parts of one’s overall physical and mental life. As one of the methods in determining the appropriate amount of sleep, this research presents a heart rate based sleep quality index. In order to evaluate sleep quality using the heart rate, sleep data from 280 subjects taken over one month are used. Their sleep data are categorized by a three-part heart rate range. After categorizing, some features are extracted, and the statistical significances are verified for these features. The results show that some features of this sleep quality index model have statistical significance. Thus, this heart rate based sleep quality index may be a useful discriminator of sleep.

Keywords: sleep, sleep quality, heart rate, statistical analysis

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Authors: Abdullahi Yahaya Musa, Harun Rann Bakari

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The study examined the determinants of food security among households in Maiduguri, Metropolis, Borno State, Nigeria. The objectives of the study are to: examine the determinants of food security among households; identify the coping strategies employed by food-insecure households in Maiduguri, Metropolis, Borno State, Nigeria. The population of the study is 843,964 respondents out of which 400 respondents were sampled. The study used a self-developed questionnaire to collect data from four hundred (400) respondents. Four hundred (400) copies of questionnaires were administered and all were retrieved, making 100% return rate. The study employed descriptive and inferential statistics for data analysis. Descriptive statistics (frequency counts and percentages) was used to analyze the socio-economic characteristics of the respondents and objective four, while inferential statistics (logit regression analysis) was used to analyze one. Four hundred (400) copies of questionnaires were administered and all the four hundred (400) were retrieved, making a 100% return rate. The results were presented in tables and discussed according to the research objectives. The study revealed that HHA, HHE, HHSZ, HHSX, HHAS, HHI, HHFS, HHFE, HHAC and HHCDR were the determinants of food security in Maiduguri Metropolis. Relying on less preferred foods, purchasing food on credit, limiting food intake to ensure children get enough, borrowing money to buy foodstuffs, relying on help from relatives or friends outside the household, adult family members skipping or reducing a meal because of insufficient finances and ration money to household members to buy street food were the coping strategies employed by food-insecure households in Maiduguri metropolis. The study recommended that Nigeria Government should intensify the fight against the Boko haram insurgency. This will put an end to Boko Haram Insurgency and enable farmers to return to farming in Borno state.

Keywords: internally displaced persons, food security, coping strategies, descriptive statistics, logistics regression model, odd ratio

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Authors: N. Alhazmi

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Knowing the thermodynamics properties of hydrocarbons is vital when it comes to analyzing the related chemical reaction outcomes and understanding the reaction process, especially in terms of petrochemical industrial applications, combustions, and catalytic reactions. However, measuring the thermodynamics properties experimentally is time-consuming and costly. In this paper, Gaussian process regression (GPR) has been used to directly predict the main thermodynamic properties - standard enthalpy of formation, standard entropy, and heat capacity -for more than 360 cyclic and non-cyclic alkanes, alkenes, and alkynes. A simple workflow has been proposed that can be applied to directly predict the main properties of any hydrocarbon by knowing its descriptors and chemical structure and can be generalized to predict the main properties of any material. The model was evaluated by calculating the statistical error R², which was more than 0.9794 for all the predicted properties.

Keywords: thermodynamic, Gaussian process regression, hydrocarbons, regression, supervised learning, entropy, enthalpy, heat capacity

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Authors: Lluís Bermúdez, Isabel Morillo

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Road safety involves carrying out a determined and explicit policy to reduce accidents. In the city of Barcelona, through the Local Road Safety Plan 2013-2018, in line with the framework that has been established at the European and state level, a series of preventive, corrective and technical measures are specified, with the priority objective of reducing the number of serious injuries and fatalities. In this work, based on the data from the accidents managed by the local police during the period 2010-2019, an analysis is carried out to verify whether the measures established in the Plan to reduce the accident rate have had an effect or not and to what extent. The analysis focuses on the type of accident and the type of vehicles involved. Different count regression models have been fitted, from which it can be deduced that the number of serious and fatal victims of the accidents that have occurred in the city of Barcelona has been reduced as the measures approved by the authorities.

Keywords: accident reduction, count regression models, road safety, urban traffic

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Authors: Simin Asadollahi, Amir H. Soruri, Ali Moghimi

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Nowadays, salad oils are used in many countries around the world. Therefore, it is of great importance to ensure the safety of these food products which are usually packaged in Polyethylene terephthalate (PET) bottles and come on the market. This study investigated the effects of storage time and temperature on the migration rate of phthalate compounds from PET bottle to salad oil. In more detail, migration rate of bis (2-ethylhexyl) phthalate from bottles to salad oil samples was measured in 1st, the 30th, and the 60th days of storage at a temperature of either 20 or 40 °C. At both storage temperatures, an increase in the storage time led to a statistically significant increase in the migration rate of phthalate compounds (p<.01). Regarding this, the highest migration rate occurred after 60 days of storage in to the samples. Furthermore, it was revealed bis (2-ethylhexyl) phthalate had a higher migration rate at 40 °C than at 20 °C which showed that an increase in the storage temperature would lead to an increase in the migration rate. The highest migration rate occurred in relation to salad oil stored at 40 °C and after 60 days of storage.

Keywords: salad oil, migration rate, polyethylene terephthalate, bis (2-ethylhexyl) phthalate

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Authors: Fahad Abbasi

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Flow of electrically conducting nanofluids is of pivotal importance in countless industrial and medical appliances. Fluctuations in thermophysical properties of such fluids due to variations in temperature have not received due attention in the available literature. Present investigation aims to fill this void by analyzing the flow of copper-water nanofluid with temperature dependent viscosity past a Riga plate. Strong wall suction and viscous dissipation have also been taken into account. Numerical solutions for the resulting nonlinear system have been obtained. Results are presented in the graphical and tabular format in order to facilitate the physical analysis. An estimated expression for skin friction coefficient and Nusselt number are obtained by performing linear regression on numerical data for embedded parameters. Results indicate that the temperature dependent viscosity alters the velocity, as well as the temperature of the nanofluid and, is of considerable importance in the processes where high accuracy is desired. Addition of copper nanoparticles makes the momentum boundary layer thinner whereas viscosity parameter does not affect the boundary layer thickness. Moreover, the regression expressions indicate that magnitude of rate of change in effective skin friction coefficient and Nusselt number with respect to nanoparticles volume fraction is prominent when compared with the rate of change with variable viscosity parameter and modified Hartmann number.

Keywords: heat transfer, peristaltic flows, radially varying magnetic field, curved channel

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Authors: Osamede Asowata

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The current resurgence of interest in the use of renewable energy is driven by the need to reduce the high environmental impact of fossil-based energy. The aim of this paper is to evaluate the effect of a stationary PV panel on the charging rate of deep-cycle valve regulated lead-acid (DCVRLA) batteries. Stationary PV panels are set to a fixed tilt and orientation angle, which plays a major role in dictating the output power of a PV panel and subsequently on the charging time of a DCVRLA battery. In a basic PV system, an energy storage device that stores the power from the PV panel is necessary due to the fluctuating nature of the PV voltage caused by climatic conditions. The charging and discharging times of a DCVRLA battery were determined for a twelve month period from January through December 2012. Preliminary results, which include regression analysis (R2), conversion-time per week and work-time per day, indicate that a 36 degrees tilt angle produces a good charging rate for a latitude of 26 degrees south throughout the year.

Keywords: tilt and orientation angles, solar chargers, PV panels, storage devices, direct solar radiation.

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Authors: Wanatchapong Kongkaew

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This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

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Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

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Authors: Chi-Yueh Hsu, Hsuan-Liang Hsu, Hsiu-Hui Chiang

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This study is to explore the relationship between the conspicuous consumption, leisure involvement and physical and mental health, and to understand the prediction of conspicuous consumption and leisure involvement to physical and mental health. The data was collected and analysed by purposive sampling, and the research objects were the dog walkers in Taiwan area. A total of 300 questionnaires were issued and after shaving the invalid questionnaire, a total of 246 valid samples were collected, and the effective rate was 82%.. The data were analyzed by correlation analysis and multiple stepwise regression analysis. The results showed that there was a significant correlation between conspicuous consumption and leisure involvement, and the conspicuous consumption and leisure involvement of dog walkers have a significant impact on physical and mental health, especially in self-expression, attractiveness and centrality of leisure involvement have a significant impact on physical and mental health.

Keywords: walking dog, attractiveness, self-expression, multiple stepwise regression analysis

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Authors: Pallavi Gajjar, Vinayak Malhotra

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Rockets have been known to have played a predominant role in spacecraft propulsion. The quintessential aspect of combustion-related requirements of a rocket engine is the minimization of the surrounding risks/hazards. Over time, it has become imperative to understand the combustion rate variation in presence of external energy source(s). Rocket propulsion represents a special domain of chemical propulsion assisted by high speed flows in presence of acoustics and thermal source(s). Jet noise leads to a significant loss of resources and every year a huge amount of financial aid is spent to prevent it. External heat source(s) induce high possibility of fire risk/hazards which can sufficiently endanger the operation of a space vehicle. Appreciable work had been done with justifiable simplification and emphasis on the linear variation of external energy source(s), which yields good physical insight but does not cater to accurate predictions. Present work experimentally attempts to understand the correlation between inter-energy conversions with the non-linear placement of external energy source(s). The work is motivated by the need to have better fire safety and enhanced combustion. The specific objectives of the work are a) To interpret the related energy transfer for combustion in presence of alternate external energy source(s) viz., thermal and acoustic, b) To fundamentally understand the role of key controlling parameters viz., separation distance, the number of the source(s), selected configurations and their non-linear variation to resemble real-life cases. An experimental setup was prepared using incense sticks as potential fuel and paraffin wax candles as the external energy source(s). The acoustics was generated using frequency generator, and source(s) were placed at selected locations. Non-equidistant parametric experimentation was carried out, and the effects were noted on regression rate changes. The results are expected to be very helpful in offering a new perspective into futuristic rocket designs and safety.

Keywords: combustion, acoustic energy, external energy sources, regression rate

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Authors: Jonathan Olusegun Famoroti, Mathew Ekundayo Rotimi, Mishelle Doorasamy

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This study offers an empirical investigation into some selected macroeconomic drivers of the monetary policy rate in member countries of the West African Monetary Zone (WAMZ), considering both internal and external variables. We employed Autoregressive Distributed Lag (ARDL) to carry out the investigation between monetary policy and some macroeconomic variables in both the long-run and short-run relationship. The results suggest that the drivers of the policy rate in this zone, in the long run, include, among others, global oil price, exchange rate, inflation rate, and gross domestic product, while in the short run, federal fund rate, trade openness, exchange rate, inflation rate, and gross domestic product are core determinants of the policy rate. Therefore, in order to ensure long-run stability in the policy rate among the members’ states, these drivers should be given closer consideration so that the trajectory for effective structure can be designed and fused into the economic structure and policy frameworks accordingly.

Keywords: monetary policy rate, macroeconomic variables, WAMZ, ARDL

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Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

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Authors: Libena Cernohorska

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The goal of this paper is to specify factors influencing the profitability of selected banks. Next, a model will be created to help establish variables that have a demonstrable influence on the development of the selected banks' profitability ratios. Czech banks and their international parent companies were selected for analyzing profitability. Banks categorized as large banks (according to the Czech National Bank's system, which ranks banks according to balance sheet total) were selected to represent the Czech banks. Two ratios, the return on assets ratio (ROA) and the return on equity ratio (ROE) are used to assess bank profitability. Six endogenous and four external indicators were selected from among other factors that influence bank profitability. The data analyzed were for the years 2001 – 2013. First, correlation analysis, which was supposed to eliminate correlated values, was conducted. A large number of correlated values were established on the basis of this analysis. The strongly correlated values were omitted. Despite this, the subsequent regression analysis of profitability for the individual banks that were selected did not confirm that the selected variables influenced their profitability. The studied factors' influence on bank profitability was demonstrated only for Československá Obchodní Banka and Société Générale using regression analysis. For Československá Obchodní Banka, it was demonstrated that inflation level and the amount of the central bank's interest rate influenced the return on assets ratio and that capital adequacy and market concentration influenced the return on equity ratio for Société Générale.

Keywords: banks, profitability, regression analysis, ROA, ROE

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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