Search results for: recursive least squares

Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

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In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy C-Means methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc).

Keywords: defuzzification, floating search, fuzzy clustering, Zernike moments

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Authors: O. Poleshchuk, E. Komarov

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This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.

Keywords: interval type-2 fuzzy sets, fuzzy regression, weighted interval

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Authors: Ying Zhao, Xingyan Bin

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Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data.

Keywords: semi-supervised clustering, hierarchical agglomerative clustering, reference trees, distance constraints

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Authors: Mohammed S. Al-Rawi, J. Bastos, J. Rodriguez

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Object detection and object recognition are essential components of every computer vision system. Despite the high computational complexity and other problems related to numerical stability and accuracy, Zernike moments of 2D images (ZMs) have shown resilience when used in object recognition and have been used in various image analysis applications. In this work, we propose a novel method for computing ZMs via Fast Fourier Transform (FFT). Notably, this is the first algorithm that can generate ZMs up to extremely high orders accurately, e.g., it can be used to generate ZMs for orders up to 1000 or even higher. Furthermore, the proposed method is also simpler and faster than the other methods due to the availability of FFT software and/or hardware. The accuracies and numerical stability of ZMs computed via FFT have been confirmed using the orthogonality property. We also introduce normalizing ZMs with Neumann factor when the image is embedded in a larger grid, and color image reconstruction based on RGB normalization of the reconstructed images. Astonishingly, higher-order image reconstruction experiments show that the proposed methods are superior, both quantitatively and subjectively, compared to the q-recursive method.

Keywords: Chebyshev polynomial, fourier transform, fast algorithms, image recognition, pseudo Zernike moments, Zernike moments

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Authors: M. Bartal

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A large empirical evidence points to sizable lifetime earnings losses associated with the displacement of tenured workers. The causes of these losses are still not well-understood. Existing explanations are heavily based on human capital depreciation during non-employment spells. In this paper, a new avenue is explored. Evidence on the role of household liquidity constraints in accounting for the persistence of post-displacement earning losses is provided based on SIPP data. Then, a directed search and matching model with endogenous human capital and wealth accumulation is introduced. The model is computationally tractable thanks to its block-recursive structure and highlights a non-trivial, yet intuitive, interaction between wealth and human capital. Constrained workers tend to accept jobs with low firm-sponsored training because the latter are (endogenously) easier to find. This new channel provides a plausible explanation for why young (highly constrained) workers suffer persistent scars after displacement. Finally, the model is calibrated on US data to show that the interplay between wealth and human capital is crucial to replicate the observed lifecycle pattern of earning losses. JEL— E21, E24, J24, J63.

Keywords: directed search, human capital accumulation, job displacement, wealth accumulation

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Authors: Z. Masoumi, B. Moaveni

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This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied.

Keywords: Kalman filter (KF), least square estimation (LSE), structural health monitoring (SHM), structural system identification

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Authors: Abubakar Danbaba

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Sudoku squares have been widely used to design an experiment where each treatment occurs exactly once in each row, column or sub-block. For some experiments, the size of row (or column or sub-block) may be larger than the number of treatments. Since each treatment appears only once in each row (column or sub-block) with an additional empty cell such designs are partially balanced Sudoku designs (PBSD) with NP-complete structures. This paper proposed methods for constructing PBSD of prime order of treatments by a modified Kronecker product and swap of matrix row (or column) in cyclic order. In addition, linear model and procedure for the analysis of data for PBSD are proposed.

Keywords: sudoku design, partial sudoku, NP-complete, Kronecker product, row and column swap

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Authors: Resul Öztürk

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Experiential marketing is one of the marketing approaches that offers an exceptional framework to integrate elements of experience and entertainment in a product or service. Experiential marketing is defined as a memorable experience that goes deeply into the customer’s mind. Besides that, customer satisfaction is defined as an emotional response to the experiences provided by and associated with particular products or services purchased. Thus, experiential marketing activities can affect the level of customer satisfaction and loyalty. In this context, the research aims to explore the relationship among experiential marketing, customer satisfaction and customer loyalty among the cosmetic products customers in Konya. The partial least squares (PLS) method is used to analyse the survey data. The present study’s findings revealed have that experiential marketing has been a significant predictor of customer satisfaction and customer loyalty, and also experiential marketing has a significantly positive effect on customer satisfaction and customer loyalty.

Keywords: experiential marketing, customer satisfaction, customer loyalty, social sciences

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Authors: Sofia Ayouche, Rachid Ellaia, Rajae Aboulaich

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Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve.

Keywords: optimization, zero-coupon curve, Nelson-Siegel-Svensson, particle swarm optimization, Nelder-Mead algorithm

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Authors: Ashwani Kumar

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GPS plays an important role in everyday life for safe and convenient transportation. While pedestrians use hand held devices to know their position in a city, vehicles in intelligent transport systems use relatively sophisticated GPS receivers for estimating their current position. However, in urban areas where the GPS satellites are occluded by tall buildings, trees and reflections of GPS signals from nearby vehicles, GPS position estimation becomes poor. In this work, an exhaustive GPS data is collected at a single point in urban area under different times of day and under dynamic environmental conditions. The data is analyzed and statistical refinement methods are used to obtain optimal position estimate among all the measured positions. The results obtained are compared with publically available datasets and obtained position estimation refinement results are promising.

Keywords: global positioning system, statistical approach, intelligent transport systems, least squares estimation

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Authors: M. Ouassaf, S. Belaid

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A series of quinoline derivatives with antimalarial activity were subjected to two-dimensional quantitative structure-activity relationship (2D-QSAR) studies. Three models were implemented using multiple regression linear MLR, a regression partial least squares (PLS), nonlinear regression (MNLR), to see which descriptors are closely related to the activity biologic. We relied on a principal component analysis (PCA). Based on our results, a comparison of the quality of, MLR, PLS, and MNLR models shows that the MNLR (R = 0.914 and R² = 0.835, RCV= 0.853) models have substantially better predictive capability because the MNLR approach gives better results than MLR (R = 0.835 and R² = 0,752, RCV=0.601)), PLS (R = 0.742 and R² = 0.552, RCV=0.550) The model of MNLR gave statistically significant results and showed good stability to data variation in leave-one-out cross-validation. The obtained results suggested that our proposed model MNLR may be useful to predict the biological activity of derivatives of quinoline.

Keywords: antimalarial, quinoline, QSAR, PCA, MLR , MNLR, MLR

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Authors: K. K. Soundra Pandian, Bhupendra Gupta

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In the modern era, as the application data’s are massive and complex, it needs to be secured from the adversary attack. In this context, a non-recursive key based integrated spritz stream cipher with the circulant hash function using delayed feedback with carry shift register (d-FCSR) is proposed in this paper. The novelty of this proposed stream cipher algorithm is to engender the improved keystream using d-FCSR. The proposed algorithm is coded using Verilog HDL to produce dynamic binary key stream and implemented on commercially available FPGA device Virtex 5 xc5vlx110t-2ff1136. The implementation of stream cipher using d-FCSR on the FPGA device operates at a maximum frequency of 60.62 MHz. It achieved the data throughput of 492 Mbps and improved in terms of efficiency (throughput/area) compared to existing techniques. This paper also briefs the cryptanalysis of proposed circulant hash value based spritz stream cipher using d-FCSR is against the adversary attack on a hardware platform for the hardware based cryptography applications.

Keywords: cryptography, circulant function, field programmable gated array, hash value, spritz stream cipher

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Authors: Nazima Ellahi, Danish Ramzan

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There is a growing trend among development economists regarding the importance of financial sector for economic development and growth activities. The development thus introduced, helps to promote welfare effects and poverty alleviation. This study is an attempt to find the nature of link between Islamic banking financing and development of output growth for Pakistan. Time series data set has been utilized for a time period ranging from 1990 to 2010. Following the Phillip Perron (PP) and Augmented Dicky Fuller (ADF) test of unit root this study applied Ordinary Least Squares (OLS) method of estimation and found encouraging results in favor of promoting the Islamic banking practices in Pakistan.

Keywords: Islamic finance, poverty alleviation, economic growth, finance, commerce

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Authors: Shagufta Tabassum, V. P. Pawar

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The study of static dielectric properties in a binary mixture of 1,2 dichloroethane (DE) and n,n dimethylformamide (DMF) polar liquids has been carried out in the frequency range of 10 MHz to 30 GHz for 11 different concentration using time domain reflectometry technique at 10ºC temperature. The dielectric relaxation study of solute-solvent mixture at microwave frequencies gives information regarding the creation of monomers and multimers as well as interaction between the molecules of the binary mixture. The least squares fit method is used to determine the values of dielectric parameters such as static dielectric constant (ε₀), dielectric constant at high frequency (ε_∞) and relaxation time (τ).

Keywords: shagufta shaikhexcess parameters, relaxation time, static dielectric constant, time domain reflectometry

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Authors: C. Milind Caspar, Valtonia Octavio Massingue, K. Maneesh Reddy, K. V. Ramesh

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Gas holdup data were obtained from measured pressure drop values in a gas-liquid upflow bubble column in the presence of string of hemispheres promoter internal. The parameters that influenced the gas holdup are gas velocity, liquid velocity, promoter rod diameter, pitch and base diameter of hemisphere. Tap water was used as liquid phase and nitrogen as gas phase. About 26 percent in gas holdup was obtained due to the insertion of promoter in in the present study in comparison with empty conduit. Pitch and rod diameter have not shown any influence on gas holdup whereas gas holdup was strongly influenced by gas velocity, liquid velocity and hemisphere base diameter. Correlation equation was obtained for the prediction of gas holdup by least squares regression analysis.

Keywords: bubble column, gas-holdup, two-phase flow, turbulent promoter

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Authors: Keitumetse Gaesirwe

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This study investigated the effect of corporate social responsibility (CSR) on brand equity. Specific objectives include examining the connections between ethics and philanthropic constructs of CSR and brand loyalty in the telecommunication industry in South Africa. A convenience sampling technique was used, and closed-ended questionnaires were administered to 800 research participants across the nine provinces of South Africa. Data collected from the field was analyzed using inferential statistics (Ordinary Least Squares regression and correlation analysis) as well as descriptive statistics. Findings show positive and significant connections between the constructs of CSR and brand loyalty. The implications of the findings indicate that keeping ethical and philanthropy standards can be a source of competitive advantage and guarantee brand loyalty for telecommunication companies in South Africa.

Keywords: CSR, brand awareness, telecommunication industry, COVID-19, South Africa

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Authors: Danika Pieters

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Due to changing societal and economic demands, organizational change has become increasingly prevalent in work life. While a long time change research has focused on the effects of single discrete change events on different employee outcomes such as job satisfaction and organizational commitment, a nascent research stream has begun to look into the potential cumulative effects of change in the context of continuous intense reforms. This case study of a large Belgian public organization aims to add to this growing literature by examining how the frequency and diversity of past changes impact employees’ appraisals of a newly introduced change. Twelve hundred survey results were analyzed using standard ordinary least squares regression. Results showed a correlation between high past change frequency and diversity and a negative appraisal of the new change. Implications for practitioners and future research are discussed.

Keywords: change frequency, change diversity, organizational changes, change appraisal, change evaluation

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Authors: Z. Jannoo, B. W. Yap, N. Auchoybur, M. A. Lazim

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The two common approaches to Structural Equation Modeling (SEM) are the Covariance-Based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM). There is much debate on the performance of CB-SEM and PLS-SEM for small sample size and when distributions are non-normal. This study evaluates the performance of CB-SEM and PLS-SEM under normality and non-normality conditions via a simulation. Monte Carlo Simulation in R programming language was employed to generate data based on the theoretical model with one endogenous and four exogenous variables. Each latent variable has three indicators. For normal distributions, CB-SEM estimates were found to be inaccurate for small sample size while PLS-SEM could produce the path estimates. Meanwhile, for a larger sample size, CB-SEM estimates have lower variability compared to PLS-SEM. Under non-normality, CB-SEM path estimates were inaccurate for small sample size. However, CB-SEM estimates are more accurate than those of PLS-SEM for sample size of 50 and above. The PLS-SEM estimates are not accurate unless sample size is very large.

Keywords: CB-SEM, Monte Carlo simulation, normality conditions, non-normality, PLS-SEM

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Authors: Shokrya Saleh A. Alshqaq, Abdullah Ali H. Ahmadini

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The Schwarz information criterion (SIC) is a popular tool for selecting the best variables in regression datasets. However, SIC is defined using an unbounded estimator, namely, the least-squares (LS), which is highly sensitive to outlying observations, especially bad leverage points. A method for robust variable selection based on SIC for linear regression models is thus needed. This study investigates the robustness properties of SIC by deriving its influence function and proposes a robust SIC based on the MM-estimation scale. The aim of this study is to produce a criterion that can effectively select accurate models in the presence of vertical outliers and high leverage points. The advantages of the proposed robust SIC is demonstrated through a simulation study and an analysis of a real dataset.

Keywords: influence function, robust variable selection, robust regression, Schwarz information criterion

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Authors: Eric S. Parilla, Marc Edward Abadilla

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The study aims to determine the effect of awareness of business students towards cryptocurrencies and the readiness of universities and colleges to accept cryptocurrencies as a medium of exchange, considering the mediation of business students’ attitudes. The research used partial least squares structural equation modeling (PLS-SEM) and deployed a questionnaire attuned to the awareness and attitudes of business students towards cryptocurrencies and readiness and compatibility of use in universities and colleges in Ilocos Norte. The output of the investigation revealed that awareness of business students is not correlated to the readiness of universities and colleges, which means that even though students understand cryptocurrencies, it is not an assurance that universities and colleges are ready to accept them as the medium of exchange. The study proposes that training and seminars for business students and professionals should be conducted to expand understanding and acceptance of cryptocurrencies.

Keywords: cryptocurrencies, awareness, readiness, attitudes

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Authors: Lincy Stephen, N. Yogesh, G. Vasantharajan, V. Subramanian

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This paper presents the design and simulation of a five band metamaterial absorber at microwave frequencies. The absorber unit cell consists of squares and strips arranged as the top layer and a metallic ground plane as the bottom layer on a dielectric substrate. Simulation results show five near perfect absorption bands at 3.15 GHz, 7.15 GHz, 11.12 GHz, 13.87 GHz, and 16.85 GHz with absorption magnitudes 99.68%, 99.05%, 96.98%, 98.36% and 99.44% respectively. Further, the proposed absorber exhibits polarization insensitivity and wide angle receptivity. The surface current analysis is presented to explain the mechanism of absorption in the structure. With these preferable features, the proposed absorber can be excellent choice for potential applications such as electromagnetic interference (EMI) shielding, radar cross section reduction.

Keywords: electromagnetic absorber, metamaterial, multi- band, polarization insensitive, wide angle receptive

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Authors: Shu-Ching Chiu, Shu-Fang Chang

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The World Health Organization (WHO) has been actively developing intervention programs to deal with geriatric frailty. In its White Paper on Healthcare Policy 2020, the Department of Health, Bureau of Health Promotion proposed that active aging and the prevention of disability are essential for elderly people to maintain good health. The paper recommended five main policies relevant to this objective, one of which is the prevention of frailty and disability. Scholars have proposed a number of different criteria to diagnose and assess frailty; no consistent or normative standard of measurement is currently available. In addition, many methods of assessment are recursive, which can easily result in recall bias. Due to the relationship between frailty and physical fitness with regard to co-morbidity, it is important that academics optimize the criteria used to assess frailty by objectively evaluating the physical fitness of senior citizens. This study used a review of the literature to identify fitness indicators suitable for measuring frailty in the elderly. This study recommends that measurement criteria be integrated to produce an optimized predictive value for frailty score. Healthcare professionals could use this data to detect frailty at an early stage and provide appropriate care to prevent further debilitation and increase longevity.

Keywords: frailty, aging, physical fitness, optimized criteria, healthcare

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Authors: Ayten Ekin Meşe, Selahattin Şentürk, Melike Duvanoğlu

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Petroleum refineries are a highly complex process industry with continuous production and high operating costs. Physical separation of crude oil starts with the crude oil distillation unit, continues with various conversion and purification units, and passes through many stages until obtaining the final product. To meet the desired product specification, process parameters are strictly followed. To be able to ensure the quality of distillates, routine analyses are performed in quality control laboratories based on appropriate international standards such as American Society for Testing and Materials (ASTM) standard methods and European Standard (EN) methods. The cut point of distillates in the crude distillation unit is very crucial for the efficiency of the upcoming processes. In order to maximize the process efficiency, the determination of the quality of distillates should be as fast as possible, reliable, and cost-effective. In this sense, an alternative study was carried out on the crude oil distillation unit that serves the entire refinery process. In this work, studies were conducted with three different crude oil distillates which are Light Straight Run Naphtha (LSRN), Heavy Straight Run Naphtha (HSRN), and Kerosene. These products are named after separation by the number of carbons it contains. LSRN consists of five to six carbon-containing hydrocarbons, HSRN consist of six to ten, and kerosene consists of sixteen to twenty-two carbon-containing hydrocarbons. Physical properties of three different crude distillation unit products (LSRN, HSRN, and Kerosene) were determined using Near-Infrared Spectroscopy with multivariate calibration. The absorbance spectra of the petroleum samples were obtained in the range from 10000 cm⁻¹ to 4000 cm⁻¹, employing a quartz transmittance flow through cell with a 2 mm light path and a resolution of 2 cm⁻¹. A total of 400 samples were collected for each petroleum sample for almost four years. Several different crude oil grades were processed during sample collection times. Extended Multiplicative Signal Correction (EMSC) and Savitzky-Golay (SG) preprocessing techniques were applied to FT-NIR spectra of samples to eliminate baseline shifts and suppress unwanted variation. Two different multivariate calibration approaches (Partial Least Squares Regression, PLS and Genetic Inverse Least Squares, GILS) and an ensemble model were applied to preprocessed FT-NIR spectra. Predictive performance of each multivariate calibration technique and preprocessing techniques were compared, and the best models were chosen according to the reproducibility of ASTM reference methods. This work demonstrates the developed models can be used for routine analysis instead of conventional analytical methods with over 90% accuracy.

Keywords: crude distillation unit, multivariate calibration, near infrared spectroscopy, data preprocessing, refinery

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Authors: Anocha Rojanapanich

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The objective of this research is to investigate the effects of earnings management on corporate transparency of the company in Dusit area workplace via financial reporting reliability and stakeholder acceptance as independent variable. And the company in Dusit are are taken as the population and sample. The questionnaire is used to collect data. Exploratory Factor Analysis is implemented to ensure construct validity, and correlation statistic is selected to test the relationship among all variable and the ordinary least squares regression is used to explore the hypothesized. The results show that earnings management has a significant and negative impact on financial reporting reliability, stakeholder acceptance, and corporate transparency. Both financial reporting reliability and stakeholder acceptance have an important and positive effect on corporate transparency, and they are then mediators of the earnings management-corporate transparency relationships.

Keywords: dusit area workplace, earnings management, financial report, business and marketing management

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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: Dalson Figueiredo, Enivaldo Rocha, Ranulfo Paranhos, José Alexandre

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This paper analyzes the effects of institutional capacity on corruption. Methodologically, the research design combines both descriptive and multivariate statistics to examine two original datasets based on secondary data. In particular, we employ a principal component model to estimate an indicator of institutional capacity for both state audit institutions and subnational judiciary courts. Then, we estimate the effect of institutional capacity on two dependent variables: (1) incidence of administrative irregularities and (2) time elapsed to judge corruption cases. The preliminary results using ordinary least squares, negative binomial and Tobit models suggest the same conclusions: higher the institutional audit capacity, higher is the probability of detecting a corruption case. On the other hand, higher the institutional capacity of state judiciary, the lower is the time to judge corruption cases.

Keywords: institutional capacity, corruption, state level institutions, evidence from Brazil

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Authors: Yuthapoom Thanakijborisut

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This paper studies consumption insurance against the chronic illness in Thailand. The study estimates the impact of household consumption in the chronic illness on consumption growth. Chronic illness is the health care costs of a person or a household’s decision in treatment for the long term; the causes and effects of the household’s ability for smooth consumption. The chronic illnesses are measured in health status when at least one member within the household faces the chronic illness. The data used is from the Household Social Economic Panel Survey conducted during 2007 and 2012. The survey collected data from approximately 6,000 households from every province, both inside and outside municipal areas in Thailand. The study estimates the change in household consumption by using an ordinary least squares (OLS) regression model. The result shows that the members within the household facing the chronic illness would reduce the consumption by around 4%. This case indicates that consumption insurance in Thailand is quite sufficient against chronic illness.

Keywords: consumption insurance, chronic illness, health care, Thailand

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Authors: Emine Öğüt, Mehtap Öztürk, Adem Öğüt

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Affective commitment is defined as a strong belief in and acceptance of the organization’s goals and values. Organizational justice is an antecedent of the organizational commitment and it has the potential to create powerful benefits for organizations and employees alike. When perceived unfairness among employees increases, affective commitment decreases and absenteeism increases accordingly. In this research, relationships between organizational justice perception, affective commitment and absenteeism is analysed. In this regard, a field study has been conducted over the physicians working in the hospitals of the Health Ministry and University Hospitals in the province of Konya. The partial least squares (PLS) method is used to analyse the survey data. The findings of the research shows that there is a positive statistically significant relationship between organizational justice perception and affective commitment while there is a negative statistically significant relationship between organizational justice and absenteeism.

Keywords: organizational justice, affective commitment, absenteeism, healthcare management

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

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Authors: Mina Zaky Sarofim Zaky

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Experiential marketing is a form of marketing that offers a unique integration of experiential and entertainment elements into a product or service. Experiential marketing is defined as an unforgettable experience that penetrates the customer's mind. Customer satisfaction is also defined as the emotional response to the experience provided with the purchased product or service. Experiential marketing activities can, therefore, affect the level of customer satisfaction and loyalty. In this context, the study aims to determine the relationship between experiential marketing, customer satisfaction and customer loyalty in cosmetic products in Konya. The least squares method (PLS) was used to analyze the research data. Existing research has shown that experiential marketing is a significant predictor of customer satisfaction and customer loyalty, and that experiential marketing has a positive impact on customer satisfaction and customer loyalty.

Keywords: internet, marketing, tourism, tourism management corporate responsibility, employee organizational performance, internal marketing, internal customer experiential marketing, customer satisfaction, customer loyalty, social sciences

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