Search results for: multinomial endogenous switching regression

Authors: Federica Marangio

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This study looks at the triangle of knowledge: research-education-innovation as growth engine of an inclusive and sustainable society, where the research is the strategic process which allows the acquisition of knowledge, innovation appraises the knowledge acquired and the education is the enabling factor of the human capital to create entrepreneurial capital. Where does Italy and China stand in the global geography of innovation? Europe is calling on a smart, inclusive and sustainable growth through a specializing process that looks at the social and economic challenges, able to understand the characteristics of specific geographic areas. It is easily questionable why it is not as simple as it looks to come up with entrepreneurial ideas in all the geographic areas. Seen that the technology plus the human capital should be the means through which is possible to innovate and contribute to the boost of innovation culture, then the young educated people can be seen as the society changing agents and it becomes clear the importance of investigating the skills and competencies that lead to innovation. By starting innovation-based activities, other countries on an international level, are able now to be part of an healthy innovative ecosystem which is the result of a strong growth policy which enables innovation. Analyzing the geography of the innovation on a global scale, comes to light that the innovative entrepreneurship is the process which portrays the competitiveness of the regions in the knowledge-based economy as strategic process able to match intellectual capital and market opportunities. The level of innovative entrepreneurship is not only the result of the endogenous growth ability of the enterprises, but also by significant relations with other enterprises, universities, other centers of education and institutions. To obtain more innovative entrepreneurship is necessary to stimulate more synergy between all these territory actors in order to create, access and value existing and new knowledge ready to be disseminate. This study focuses on individual’s lived experience and the researcher believed that she can’t understand the human actions without understanding the meaning that they attribute to their thoughts, feelings, beliefs and so given she needed to understand the deeper perspectives captured through face-to face interaction. A case study approach will contribute to the betterment of knowledge in this field. This case study will represent a picture of the innovative ecosystem and the entrepreneurial mindset as a key ingredient of endogenous growth and a must for sustainable local and regional development and social cohesion. The case study will be realized analyzing two Chinese companies. A structured set of questions will be asked in order to gain details on what generated success or failure in the different situations with the past and at the moment of the research. Everything will be recorded not to lose important information during the transcription phase. While this work is not geared toward testing a priori hypotheses, it is nevertheless useful to examine whether the projects undertaken by the companies, were stimulated by enabling factors that, as result, enhanced or hampered the local innovation culture.

Keywords: Entrepreneurship, education, geography of innovation, education.

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Authors: Mahlet Nigussie Worku

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The main purpose of this study was to examine the effects of different leadership styles on employee engagement in Ethiopian Airlines headquarters located in Addis Ababa. Specific objectives of the study were stated to examine the effects of five leadership styles, namely transformational, transactional, democratic, lassies fair and autocratic leadership styles on employees’ engagement. The study was conducted on 288 sample sizes, and a simple random sampling technique was employed. The quantitative findings were presented and analyzed by table, ANOVA, bivariate correlation and regression model through SPSS software version 23. Out of 288 total distributed questionnaires, 280 were returned, and 8 of the returned were rejected due to missing data, while the remaining 280 responses were used for data analysis. Data was analyzed using the Statistical Package for Social Sciences (SPSS). The study employed both descriptive and explanatory research design. Correlation and regression were used to analyze the relationship and its effect between leadership Style and employee engagement. The regression results showed that transformational, transactional and democratic leadership Styles have significant contributions to employee engagement. Similarly, the transformational, transactional land democratic leadership style had a positive and strong correlation with employee engagement. However, lassies-fair and autocratic leadership styles showed a negative and insignificant effect on employee engagement. Finally, based on the findings, workable recommendations and implications for further studies were forwarded.

Keywords: leadership, autocratic leadership style, democratic leadership style, employee engagement

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Authors: Elvira Mustikawati P.H., Iis Dewi Ratih, Dita Amelia

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Bojonegoro has proclaimed a policy of zero malnutrition. Therefore, as an effort to solve the cases of malnutrition children in Bojonegoro, this study used the approach geographically Mixed Weighted Regression (MGWR) to determine the factors that influence the percentage of malnourished children under five in which factors can be divided into locally influential factor in each district and global factors that influence throughout the district. Based on the test of goodness of fit models, R2 and AIC values in GWR models are better than MGWR models. R2 and AIC values in MGWR models are 84.37% and 14.28, while the GWR models respectively are 91.04% and -62.04. Based on the analysis with GWR models, District Sekar, Bubulan, Gondang, and Dander is a district with three predictor variables (percentage of vitamin A, the percentage of births assisted health personnel, and the percentage of clean water) that significantly influence the percentage of malnourished children under five.

Keywords: GWR, MGWR, R2, AIC

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Authors: Beier Zhu, Rui Zhang, Qi Song

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Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

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Authors: Alireza Ansariyar, Safieh Laaly

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Over the last decades, the vehicle industry has shown increased interest in integrating autonomous, connected, and electrical technologies in vehicle design with the primary hope of improving mobility and road safety while reducing transportation’s environmental impact. Using the State of Maryland (M.D.) in the United States as a pilot study, this research investigates CAVs’ fuel consumption and air pollutants (C.O., PM, and NOx) and utilizes meaningful linear regression models to predict CAV’s environmental effects. Maryland transportation network was simulated in VISUM software, and data on a set of variables were collected through a comprehensive survey. The number of pollutants and fuel consumption were obtained for the time interval 2010 to 2021 from the macro simulation. Eventually, four linear regression models were proposed to predict the amount of C.O., NOx, PM pollutants, and fuel consumption in the future. The results highlighted that CAVs’ pollutants and fuel consumption have a significant correlation with the income, age, and race of the CAV customers. Furthermore, the reliability of four statistical models was compared with the reliability of macro simulation model outputs in the year 2030. The error of three pollutants and fuel consumption was obtained at less than 9% by statistical models in SPSS. This study is expected to assist researchers and policymakers with planning decisions to reduce CAV environmental impacts in M.D.

Keywords: connected and autonomous vehicles, statistical model, environmental effects, pollutants and fuel consumption, VISUM, linear regression models

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Authors: Mahlet Nigussie Worku

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The main purpose of this study was to examine the effects of different leadership styles on employee engagement in Ethiopian Airlines head quarter located in Addis Ababa. Specific objectives of the study were stated to examine the effects of five leadership styles namely transformational, transactional, democratic, lassies fair and autocratic leadership styles on employees’ engagement. The study was conducted on 288 sample size and a simple random sampling technique was employed. The quantitative findings were presented and analyzed by table, ANOVA, bivariate correlation and regression model through SPSS software version 23. Out of 288 total distributed questionnaires 280 were returned and 8 of the returned were rejected due to missing data while the remaining 280 responses were used for data analysis. Data was analyzed using the Statistical Package for Social Sciences (SPSS). The study employed both descriptive and explanatory research design. Correlation and regression were used to analyze the relationship and its effect between leadership Style and employee’s engagement. The regression results showed that transformational, transactional and democratic leadership Styles have significant contribution for employee’s engagement. Similarly transformational, transactional land democratic leadership style had a positive and strong correlation with employee’s engagement. However lassies-fair and autocratic leadership style showed negative and insignificant effect on employee engagement. Finally, based on the findings, workable recommendations and implications for further studies were forwarded

Keywords: leadership, leadership style, employee engagement, autocratic leadership styles

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

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Authors: Jamilatuzzahro, Rezzy Eko Caraka

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The yield curve is the plot of the yield to maturity of zero-coupon bonds against maturity. In practice, the yield curve is not observed but must be extracted from observed bond prices for a set of (usually) incomplete maturities. There exist many methodologies and theory to analyze of yield curve. We use two methods (the Nelson-Siegel Method, the Svensson Method, and the SVR method) in order to construct and compare our zero-coupon yield curves. The objectives of this research were: (i) to study the adequacy of NSS model and SVR to Indonesian government bonds data, (ii) to choose the best optimization or estimation method for NSS model and SVR. To obtain that objective, this research was done by the following steps: data preparation, cleaning or filtering data, modeling, and model evaluation.

Keywords: support vector regression, Nelson-Siegel-Svensson, yield curve, Indonesian government

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Authors: Mukund Ramchandra Mogarekar, Pankaj Kumar, Shraddha V. More

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Background: Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), Apo B, and lipoprotein(a) was found as atherogenic factors while high-density lipoprotein cholesterol (HDL-C) was anti-atherogenic. Methods and Results: The study group consists of 40 MI subjects as cases and 40 healthy as controls. Monocytic PON 2 Lactonase (LACT) activity was measured by using Dihydrocoumarine (DHC) as substrate. Phenotyping was done by method of Mogarekar MR et al, serum AOPP by modified method of Witko-Sarsat V et al and Apo B by Turbidimetric immunoassay. PON 2 LACT activities were significantly lower (p< 0.05) and AOPPs & Apo B were higher in MI subjects (p> 0.05). Trimodal distribution of QQ, QR & RR phenotypes of study population showed no significant difference among cases and controls (p> 0.05). Univariate binary logistic regression analysis showed independent association of TC, HDL, LDL, AOPP, Apo B, and PON 2 LACT activity with MI and multiple forward binary logistic regression showed PON 2 LACT activity and serum Apo B as an independent predictor of MI. Conclusions- Decrease in PON 2 LACT activity in MI subjects than in controls suggests increased oxidative stress in MI which is reflected by significantly increased AOPP and Apo B. PON 1 polymorphism of QQ, QR and RR showed no significant difference in protection against MI. Univariate and multiple forward binary logistic regression showed PON 2 LACT activity and serum Apo B as an independent predictor of MI.

Keywords: advanced oxidation protein products, apolipoprotein-B, myocardial infarction, paraoxonase 2 lactonase

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Authors: Adem Peker, Yüksel Eroğlu, İsmail Ay

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As the information and communication technologies have become embedded in everyday life of adolescents, both their possible benefits and risks to adolescents are being identified. The information and communication technologies provide opportunities for adolescents to connect with peers and to access to information. However, as with other social connections, users of information and communication devices have the potential to meet and interact with in harmful ways. One emerging example of such interaction is cyber bullying. Cyber bullying occurs when someone uses the information and communication technologies to harass or embarrass another person. Cyber bullying can take the form of malicious text messages and e-mails, spreading rumours, and excluding people from online groups. Cyber bullying has been linked to psychological problems for cyber bullies and victims. Therefore, it is important to determine how internet addiction contributes to cyber bullying. Building on this question, this study takes a closer look at the relationship between internet addiction and cyber bullying. For this purpose, in this study, based on descriptive relational model, it was hypothesized that loss of control, excessive desire to stay online, and negativity in social relationships, which are dimensions of internet addiction, would be associated positively with cyber bullying and victimization. Participants were 383 high school students (176 girls and 207 boys; mean age, 15.7 years). Internet addiction was measured by using Internet Addiction Scale. The Cyber Victim and Bullying Scale was utilized to measure cyber bullying and victimization. The scales were administered to the students in groups in the classrooms. In this study, stepwise regression analyses were utilized to examine the relationships between dimensions of internet addiction and cyber bullying and victimization. Before applying stepwise regression analysis, assumptions of regression were verified. According to stepwise regression analysis, cyber bullying was predicted by loss of control (β=.26, p<.001) and negativity in social relationships (β=.13, p<.001). These variables accounted for 9 % of the total variance, with the loss of control explaining the higher percentage (8 %). On the other hand, cyber victimization was predicted by loss of control (β=.19, p<.001) and negativity in social relationships (β=.12, p<.001). These variables altogether accounted for 8 % of the variance in cyber victimization, with the best predictor loss of control (7 % of the total variance). The results of this study demonstrated that, as expected, loss of control and negativity in social relationships predicted cyber bullying and victimization positively. However, excessive desire to stay online did not emerge a significant predictor of both cyberbullying and victimization. Consequently, this study would enhance our understanding of the predictors of cyber bullying and victimization since the results proposed that internet addiction is related with cyber bullying and victimization.

Keywords: cyber bullying, internet addiction, adolescents, regression

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Authors: Mohammad Abu-Shaira, Weishi Shi

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Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

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Authors: Soran Tarkhani

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A glance at social media shows that Russia's invasion of Ukraine receives considerable support among Arabs. This significant support for the Russian invasion of Ukraine is puzzling since most Arab leaders openly condemned the Russian invasion through the UN ES‑11/4 Resolution, and Arabs are among the first who experienced the devastating consequences of war firsthand. This article tries to answer this question by using multiple regression to analyze the online content of Arab responses to Russia's invasion of Ukraine on seven major news networks: CNN Arabic, BBC Arabic, Sky News Arabic, France24 Arabic, DW, Aljazeera, and Al-Arabiya. The article argues that the underlying reason for this Arab support is a reaction to the common anti-Western sentiments among Arabs. The empirical result from regression analysis supports the central arguments and uncovers the motivations behind the endorsement of the Russian invasion of Ukraine and the opposing Ukraine by many Arabs.

Keywords: Ukraine, Russia, Arabs, Ukrainians, Russians, Putin, invasion, Europe, war

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Authors: B. H. Lee

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Nowadays, a grid-connected photovoltaic (PV) inverter is adopted in various places like as home, factory, because grid-connected PV inverter can reduce total power consumption by supplying electricity from PV array. In this paper, design and implementation of a 300 W grid-connected PV inverter are described. It is implemented with TI Piccolo DSP core and operated at 100 kHz switching frequency in order to reduce harmonic contents. The maximum operating input voltage is up to 45 V. The characteristics of the designed system that include maximum power point tracking (MPPT), single operation and battery charging are verified by simulation and experimental results.

Keywords: design, grid-connected, implementation, photovoltaic

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Authors: Oluwatosin M. A. Jesuyon

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In order to propose a simpler tool that eliminates the age-long problems associated with the traditional index method for selection of multiple traits in broilers, the Barttlet factor regression equation is being proposed as an alternative selection tool. 100 day-old chicks each of Arbor Acres (AA) and Annak (AN) broiler strains were obtained from two rival hatcheries in Ibadan Nigeria. These were raised in deep litter system in a 56-day feeding trial at the University of Ibadan Teaching and Research Farm, located in South-west Tropical Nigeria. The body weight and body dimensions were measured and recorded during the trial period. Eight (8) zoometric measurements namely live weight (g), abdominal circumference, abdominal length, breast width, leg length, height, wing length and thigh circumference (all in cm) were recorded randomly from 20 birds within strain, at a fixed time on the first day of the new week respectively with a 5-kg capacity Camry scale. These records were analyzed and compared using completely randomized design (CRD) of SPSS analytical software, with the means procedure, Factor Scores (FS) in stepwise Multiple Linear Regression (MLR) procedure for initial live weight equations. Bartlett Factor Score (BFS) analysis extracted 2 factors for each strain, termed Body-length and Thigh-meatiness Factors for AA, and; Breast Size and Height Factors for AN. These derived orthogonal factors assisted in deducing and comparing trait-combinations that best describe body conformation and Meatiness in experimental broilers. BFS procedure yielded different body conformational traits for the two strains, thus indicating the different economic traits and advantages of strains. These factors could be useful as selection criteria for improving desired economic traits. The final Bartlett Factor Regression equations for prediction of body weight were highly significant with P < 0.0001, R2 of 0.92 and above, VIF of 1.00, and DW of 1.90 and 1.47 for Arbor Acres and Annak respectively. These FSR equations could be used as a simple and potent tool for selection during poultry flock improvement, it could also be used to estimate selection index of flocks in order to discriminate between strains, and evaluate consumer preference traits in broilers.

Keywords: alternative selection tool, Bartlet factor regression model, consumer preference trait, linear and body measurements, live body weight

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Authors: Fidelis Chigondo

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This study focuses on investigating the effectiveness of watermelon rind in phenol removal from aqueous solution. The effects of various parameters (pH, initial phenol concentration, biosorbent dosage and contact time) on phenol adsorption were investigated. The pH of 2, initial phenol concentration of 40 ppm, the biosorbent dosage of 0.6 g and contact time of 6 h also deduced to be the optimum conditions for the adsorption process. The maximum phenol removal under optimized conditions was 85%. The sorption data fitted to the Freundlich isotherm with a regression coefficient of 0.9824. The kinetics was best described by the intraparticle diffusion model and Elovich Equation with regression coefficients of 1 and 0.8461 respectively showing that the reaction is chemisorption on a heterogeneous surface and the intraparticle diffusion rate only is the rate determining step. The study revealed that watermelon rind has a potential of removing phenol from industrial wastewaters.

Keywords: biosorption, phenol, biosorbent, watermelon rind

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Authors: Thauane Silva, Gabrielle do Vale, Andre Ferreira, Marilda Siqueira, Thiago Moreno L. Souza, Milene D. Miranda

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The exposure of A(H1N1)pdm09-infected epithelial cells (HeLa) to HIV-1 viral particles, or its gp120, enhanced interferon-induced transmembrane protein (IFITM3) content, a viral restriction factor (RF), resulting in a decrease in influenza replication. The gp120 binds to CCR5 (R5) or CXCR4 (X4) cell receptors during HIV-1 infection. Then, it is possible that the endogenous ligands of these receptors also modulate the expression of IFITM3 and other cellular factors that restrict influenza virus replication. Thus, the aim of this study is to analyze the role of cellular receptors R5 and X4 in modulating RFs in order to inhibit the replication of the influenza virus. A549 cells were treated with 2x effective dose (ED50) of endogenous R5 or X4 receptor agonists, CCL3 (20 ng/ml), CCL4 (10 ng/ml), CCL5 (10 ng/ml) and CXCL12 (100 ng/mL) or exogenous agonists, gp120 Bal-R5, gp120 IIIB-X4 and its mutants (5 µg/mL). The interferon α (10 ng/mL) and oseltamivir (60 nM) were used as a control. After 24 h post agonists exposure, the cells were infected with virus influenza A(H3N2) at 2 MOI (multiplicity of infection) for 1 h. Then, 24 h post infection, the supernatant was harvested and, the viral titre was evaluated by qRT-PCR. To evaluate IFITM3 and SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1) protein levels, A549 were exposed to agonists for 24 h, and the monolayer was lysed with Laemmli buffer for western blot (WB) assay or fixed for indirect immunofluorescence (IFI) assay. In addition to this, we analyzed other RFs modulation in A549, after 24 h post agonists exposure by customized RT² Profiler Polymerase Chain Reaction Array. We also performed a functional assay in which SAMHD1-knocked-down, by single-stranded RNA (siRNA), A549 cells were infected with A(H3N2). In addition, the cells were treated with guanosine to assess the regulatory role of dNTPs by SAMHD1. We found that R5 and X4 agonists inhibited influenza replication in 54 ± 9%. We observed a four-fold increase in SAMHD1 transcripts by RFs mRNA quantification panel. After 24 h post agonists exposure, we did not observe an increase in IFITM3 protein levels through WB or IFI assays, but we observed an upregulation up to three-fold in the protein content of SAMHD1, in A549 exposed to agonists. Besides this, influenza replication enhanced in 20% in cell cultures that SAMDH1 was knockdown. Guanosine treatment in cells exposed to R5 ligands further inhibited influenza virus replication, suggesting that the inhibitory mechanism may involve the activation of the SAMHD1 deoxynucleotide triphosphohydrolase activity. Thus, our data show for the first time a direct relationship of SAMHD1 and inhibition of influenza replication, and provides perspectives for new studies on the signaling modulation, through cellular receptors, to induce proteins of great importance in the control of relevant infections for public health.

Keywords: chemokine receptors, gp120, influenza, virus restriction factors

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Authors: Mahdieh Omranian, Mehran Ghanbari Motlagh

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As we know, today urban growth, development, and intelligent social evolutions have been proposed in metropolises and this matter extends urban life which can have negative items besides positive and strong items. Along with research on urban life desirable development, conditions should be provided to provide the possibility of human stable development and improvement social welfare. These conditions can reinforce social, economic, and political structures related to non-expensive housing. Demand for non-expensive housing is increasing regarding to population increase and incremental urbanizing process. Therefore, the present study by precise exploration on conceptions, challenges, and strategies, should achieve an endogenous pattern and improve housing condition by looking to instant development. Therefore, the general objective of this article recognizes the existed strategies in housing and achieving desirable conditions for all social classes by sustainable development.

Keywords: housing strategies, infrastructure, metropolis, sustainable development

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Authors: Byung Ho Jung, Ji Eun Shin, Dong Hoon Lim

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Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Ya-Hui Lee, Ching-Yi Lu, Hui-Chuan Wei

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The purpose of this research was to explore the relation between proactive coping and well-being of middle-aged adults. We conducted survey research that with t-test, one way ANOVA, Pearson correlation and stepwise multiple regression to analyze. This research drew on a sample of 395 participants from the senior learning centers of Taiwan. The results provided the following findings: 1.The participants from different residence areas associated significant difference with proactive coping, but not with well-being. 2. The participants’ perceived of financial level associated significant difference with both proactive coping and well-being. 3. There was significant difference between participants’ income and well-being. 4. The proactive coping was positively correlated with well-being. 5. From stepwise multiple regression analysis showed that two dimensions of proactive coping had positive predictability. Finally, these results of this study can be provided as references for designing older adult educational programs in Taiwan.

Keywords: middle-age and older adults, learners, proactive coping, well-being

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Authors: Nada Poropat Jeletic, Gordana Hrzica

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Bilingual conversational corpora represent a meaningful and the most comprehensive data source for investigating the genuine contact phenomena in non-monitored bi-lingual speech productions. They can be particularly useful for bilingual research since some features of bilingual interaction can hardly be accessed with more traditional methodologies (e.g., elicitation tasks). The method of language sampling provides the resources for describing language interaction in a bilingual community and/or in bilingual situations (e.g. code-switching, amount of languages used, number of languages used, etc.). To capture these phenomena in genuine communication situations, such sampling should be as close as possible to spontaneous communication. Bilingual spoken corpus design is methodologically demanding. Therefore this paper aims at describing the methodological challenges that apply to the corpus design of the conversational corpus design of the Istrian Istrovenetian-Croatian Bilingual Corpus. Croatian is the first official language of the Croatian-Italian officially bilingual Istria County, while Istrovenetian is a diatopic subvariety of Venetian, a longlasting lingua franca in the Istrian peninsula, the mother tongue of the members of the Italian National Community in Istria and the primary code of informal everyday communication among the Istrian Italophone population. Within the CLARIN infrastructure, TalkBank is being used, as it provides relevant procedures for designing and analyzing bilingual corpora. Furthermore, it allows public availability allows for easy replication of studies and cumulative progress as a research community builds up around the corpus, while the tools developed within the field of corpus linguistics enable easy retrieval and analysis of information. The method of language sampling employed is kept at the level of spontaneous communication, in order to maximise the naturalness of the collected conversational data. All speakers have provided written informed consent in which they agree to be recorded at a random point within the period of one month after signing the consent. Participants are administered a background questionnaire providing information about the socioeconomic status and the exposure and language usage in the participants social networks. Recording data are being transcribed, phonologically adapted within a standard-sized orthographic form, coded and segmented (speech streams are being segmented into communication units based on syntactic criteria) and are being marked following the CHAT transcription system and its associated CLAN suite of programmes within the TalkBank toolkit. The corpus consists of transcribed sound recordings of 36 bilingual speakers, while the target is to publish the whole corpus by the end of 2020, by sampling spontaneous conversations among approximately 100 speakers from all the bilingual areas of Istria for ensuring representativeness (the participants are being recruited across three generations of native bilingual speakers in all the bilingual areas of the peninsula). Conversational corpora are still rare in TalkBank, so the Corpus will contribute to BilingBank as a highly relevant and scientifically reliable resource for an internationally established and active research community. The impact of the research of communities with societal bilingualism will contribute to the growing body of research on bilingualism and multilingualism, especially regarding topics of language dominance, language attrition and loss, interference and code-switching etc.

Keywords: conversational corpora, bilingual corpora, code-switching, language sampling, corpus design methodology

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Authors: Sachin Jain, Daniel Perez-Astudillo, Dunia A. Bachour, Antonio P. Sanfilippo

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The future of solar energy in Qatar is evolving steadily. Hence, high-quality spatial solar radiation data is of the uttermost requirement for any planning and commissioning of solar technology. Generally, two types of solar radiation data are available: satellite data and ground observations. Satellite solar radiation data is developed by the physical and statistical model. Ground data is collected by solar radiation measurement stations. The ground data is of high quality. However, they are limited to distributed point locations with the high cost of installation and maintenance for the ground stations. On the other hand, satellite solar radiation data is continuous and available throughout geographical locations, but they are relatively less accurate than ground data. To utilize the advantage of both data, a product has been developed here which provides spatial continuity and higher accuracy than any of the data alone. The popular satellite databases: National Solar radiation Data Base, NSRDB (PSM V3 model, spatial resolution: 4 km) is chosen here for merging with ground-measured solar radiation measurement in Qatar. The spatial distribution of ground solar radiation measurement stations is comprehensive in Qatar, with a network of 13 ground stations. The monthly average of the daily total Global Horizontal Irradiation (GHI) component from ground and satellite data is used for error analysis. The normalized root means square error (NRMSE) values of 3.31%, 6.53%, and 6.63% for October, November, and December 2019 were observed respectively when comparing in-situ and NSRDB data. The method is based on the Empirical Bayesian Kriging Regression Prediction model available in ArcGIS, ESRI. The workflow of the algorithm is based on the combination of regression and kriging methods. A regression model (OLS, ordinary least square) is fitted between the ground and NSBRD data points. A semi-variogram is fitted into the experimental semi-variogram obtained from the residuals. The kriging residuals obtained after fitting the semi-variogram model were added to NSRBD data predicted values obtained from the regression model to obtain the final predicted values. The NRMSE values obtained after merging are respectively 1.84%, 1.28%, and 1.81% for October, November, and December 2019. One more explanatory variable, that is the ground elevation, has been incorporated in the regression and kriging methods to reduce the error and to provide higher spatial resolution (30 m). The final GHI maps have been created after merging, and NRMSE values of 1.24%, 1.28%, and 1.28% have been observed for October, November, and December 2019, respectively. The proposed merging method has proven as a highly accurate method. An additional method is also proposed here to generate calibrated maps by using regression and kriging model and further to use the calibrated model to generate solar radiation maps from the explanatory variable only when not enough historical ground data is available for long-term analysis. The NRMSE values obtained after the comparison of the calibrated maps with ground data are 5.60% and 5.31% for November and December 2019 month respectively.

Keywords: global horizontal irradiation, GIS, empirical bayesian kriging regression prediction, NSRDB

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Authors: Khadija Mushtaq, Mian Saqib Mehmood

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This paper attempts to determine the impact of leadership style and learning orientation on organizational performance in Pakistan. A sample of 158 middle managers selected from sports and surgical factories from Sialkot. The empirical estimation is based on a multiple linear regression analysis of the relationship between leadership style, learning orientation and organizational performance. Leadership style is measure through transformational leadership and transactional leadership. The transformational leadership has insignificant impact on organizational performance. The transactional leadership has positive and significant relation with organizational performance. Learning orientation also has positive and significant relation with organizational performance. Linear regression used to estimate the relation between dependent and independent variables. This study suggests top manger should prefer continuous process for improvement for any change in system rather radical change.

Keywords: transformational leadership, transactional leadership, learning orientation, organizational performance, Pakistan

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Authors: Benjamas Penboon, Zachary Zimmer, Aree Jampaklay

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Cambodia is a country where labor migration has been consistently high. Coupled with advancing labor opportunities in urban areas, a function partly of globalization, this is resulting in massive migration out of rural areas. This is particularly true in Cambodia where there are high migration and a very large proportion of adult children living some distant from their parents. This paper explores characteristics associated with migrant providing support to parents in rural Cambodia. With reference to perspectives of family altruism and solidarity, this analysis particularly focusses on how a series of variables representing family integration and residential location associates with intergenerational monetary and instrumental support from migrants. The study hypothesizes that migrants are more likely to provide support when parents are in need, and there are no alternative means of support. Data come from The Rural Household Survey (N=3,713), part of the 2011 Cambodian Rural Urban Migration Project (CRUMP). Multilevel multinomial models indicate international migrants are likely to give money, while internal migrants are likely to provide both money and instrumental support, especially when migrants have no sibling and their parent in poor health status. In addition, employed migrants are two times providing monetary compared to those unemployed. Findings elucidate the decision to which and why support occurs more often when no other source of support exists and also depends on the ability to provide of migrants themselves.

Keywords: migration, left-behind parent, intergenerational relations, support, rural, Cambodia

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Authors: Charlotte K. Lee, Henrique R. N. Aguiar, Ralph Smith, James Baldock, Sam Botchey

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Background: Femoral neck stress fractures (FNSF) are uncommon, making up 1 to 7.2% of stress fractures in healthy subjects. FNSFs are prevalent in young women, military recruits, endurance athletes, and individuals with energy deficiency syndrome or female athlete triad. Presentation is often non-specific and is often misdiagnosed following the initial examination. There is limited research addressing the return–to–activity time after FNSF. Previous studies have demonstrated prognostic time predictions based on various imaging techniques. Here, (1) OxSport clinic FNSF practice standards are retrospectively reviewed, (2) FNSF cohort demographics are examined, (3) Regression models were used to predict return–to–activity prognosis and consequently determine bone stress risk factors. Methods: Patients with a diagnosis of FNSF attending Oxsport clinic between 01/06/2020 and 01/01/2020 were selected from the Rheumatology Assessment Database Innovation in Oxford (RhADiOn) and OxSport Stress Fracture Database (n = 14). (1) Clinical practice was audited against five criteria based on local and National Institute for Health Care Excellence guidance, with a 100% standard. (2) Demographics of the FNSF cohort were examined with Student’s T-Test. (3) Lastly, linear regression and Random Forest regression models were used on this patient cohort to predict return–to–activity time. Consequently, an analysis of feature importance was conducted after fitting each model. Results: OxSport clinical practice met standard (100%) in 3/5 criteria. The criteria not met were patient waiting times and documentation of all bone stress risk factors. Importantly, analysis of patient demographics showed that of the population with complete bone stress risk factor assessments, 53% were positive for modifiable bone stress risk factors. Lastly, linear regression analysis was utilized to identify demographic factors that predicted return–to–activity time [R2 = 79.172%; average error 0.226]. This analysis identified four key variables that predicted return-to-activity time: vitamin D level, total hip DEXA T value, femoral neck DEXA T value, and history of an eating disorder/disordered eating. Furthermore, random forest regression models were employed for this task [R2 = 97.805%; average error 0.024]. Analysis of the importance of each feature again identified a set of 4 variables, 3 of which matched with the linear regression analysis (vitamin D level, total hip DEXA T value, and femoral neck DEXA T value) and the fourth: age. Conclusion: OxSport clinical practice could be improved by more comprehensively evaluating bone stress risk factors. The importance of this evaluation is demonstrated by the population found positive for these risk factors. Using this cohort, potential bone stress risk factors that significantly impacted return-to-activity prognosis were predicted using regression models.

Keywords: eating disorder, bone stress risk factor, femoral neck stress fracture, vitamin D

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Authors: Melkamu A. Zeru, Yamral M. Warkaw, Aweke A. Mitku, Muluwerk Ayele

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Introduction: Malaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia. Methods: A weighted sample of 15,239 individuals with rapid diagnosis tests was obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran's I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran's I statistic was used in identifying exposed areas. In data manipulation, machine learning was used for variable reduction and statistical software R, Stata, and Python were used for data management and analysis. The auto logistics spatial binary regression model was used to investigate the predictors of malaria. Results: The final auto logistics regression model reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR=2.401, 95 % CI: (2.125 - 2.713)]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR=52.55, 95%CI: (40.54-68.12)] followed by Beneshangul [AOR=34.95, 95%CI: (27.159 - 44.963)]. Similarly, individuals in Amhara [AOR=0.243, 95% CI:(0.1950.303],Oromiya[AOR=0.197,95%CI:(0.1580.244)],DireDawa[AOR=0.064,95%CI(0.049-0.082)],AddisAbaba[AOR=0.057,95%CI:(0.044-0.075)], Somali[AOR=0.077,95%CI:(0.059-0.097)], SNNPR[OR=0.329, 95%CI: (0.261- 0.413)] and Harari [AOR=0.256, 95%CI:(0.201 - 0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for a one-meter increase in altitude, the odds of a positive rapid diagnostic test (RDT) decrease by 1.6% [AOR = 0.984, 95% CI :( 0.984 - 0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR=1.671, 95% CI: (1.504 - 1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression. Conclusions: This study found that the incidence of malaria in Ethiopia had a spatial pattern that is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who live in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramic tiles roof houses. Moreover, using a protected anti-mosquito net reduced the risk of malaria incidence.

Keywords: malaria, Ethiopia, auto logistics, spatial model, spatial clustering

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Authors: Helena Bolini, Erick Esmerino, Adriano Cruz, Juliana Paixao

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The currently concern for health has increased demand for low-calorie ingredients and functional foods as probiotics. Understand the reasons that infer on food choice, besides a challenging task, it is important step for development and/or reformulation of existing food products. The use of appropriate multivariate statistical techniques, such as External Preference Map (PrefMap), associated with regression by Partial Least Squares (PLS) can help in determining those factors. Thus, this study aimed to determine, through PLS regression analysis, the sensory attributes considered drivers of liking in probiotic petit suisse cheeses, strawberry flavor, sweetened with different sweeteners. Five samples in same equivalent sweetness: PROB1 (Sucralose 0.0243%), PROB2 (Stevia 0.1520%), PROB3 (Aspartame 0.0877%), PROB4 (Neotame 0.0025%) and PROB5 (Sucrose 15.2%) determined by just-about-right and magnitude estimation methods, and three commercial samples COM1, COM2 and COM3, were studied. Analysis was done over data coming from QDA, performed by 12 expert (highly trained assessors) on 20 descriptor terms, correlated with data from assessment of overall liking in acceptance test, carried out by 125 consumers, on all samples. Sequentially, results were submitted to PLS regression using XLSTAT software from Byossistemes. As shown in results, it was possible determine, that three sensory descriptor terms might be considered drivers of liking of probiotic petit suisse cheese samples added with sweeteners (p<0.05). The milk flavor was noticed as a sensory characteristic with positive impact on acceptance, while descriptors bitter taste and sweet aftertaste were perceived as descriptor terms with negative impact on acceptance of petit suisse probiotic cheeses. It was possible conclude that PLS regression analysis is a practical and useful tool in determining drivers of liking of probiotic petit suisse cheeses sweetened with artificial and natural sweeteners, allowing food industry to understand and improve their formulations maximizing the acceptability of their products.

Keywords: acceptance, consumer, quantitative descriptive analysis, sweetener

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Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: cutting condition, surface roughness, decision tree, CART algorithm

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Authors: Emin Çadirli, Izzettin Yilmazer, Uğur Büyük, Hasan Kaya

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Al-2Mn-5Fe eutectic alloy (wt.%) was prepared in a graphite crucible under vacuum atmosphere. The samples were directionally solidified upward at a constant temperature gradient in four different of growth rates by using a Bridgman method. The values of eutectic spacing were measured from longitudinal and transverse sections of the samples. The dependence of eutectic spacing on the growth rate was determined by using linear regression analysis. The microhardness and tensile strength of the studied alloy also were measured from directionally solidified samples. The dependency of the microhardness and tensile strength for directionally solidified Al-2Mn-5Fe eutectic alloy on the growth rate were investigated and the relationships between them were experimentally obtained by using regression analysis. The results obtained in present work were compared with the previous similar experimental results obtained for binary and ternary alloys.

Keywords: eutectic alloy, microhardness, microstructure, tensile strength

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Authors: Fariza Yunus, Jasmee Jaafar, Zamalia Mahmud, Nurul Nisa’ Khairul Azmi, Nursalleh K. Chang, Nursalleh K. Chang

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Variation of air temperature from one place to another is cause by air temperature controls. In general, the most important control of air temperature is elevation. Another significant independent variable in estimating air temperature is the location of meteorological stations. Distances to coastline and land use type are also contributed to significant variations in the air temperature. On the other hand, in homogeneous terrain direct interpolation of discrete points of air temperature work well to estimate air temperature values in un-sampled area. In this process the estimation is solely based on discrete points of air temperature. However, this study presents that air temperature controls also play significant roles in estimating air temperature over homogenous terrain of Peninsular Malaysia. An Inverse Distance Weighting (IDW) interpolation technique was adopted to generate continuous data of air temperature. This study compared two different datasets, observed mean monthly data of T, and estimation error of T–T’, where T’ estimated value from a multiple regression model. The multiple regression model considered eight independent variables of elevation, latitude, longitude, coastline, and four land use types of water bodies, forest, agriculture and build up areas, to represent the role of air temperature controls. Cross validation analysis was conducted to review accuracy of the estimation values. Final results show, estimation values of T–T’ produced lower errors for mean monthly mean air temperature over homogeneous terrain in Peninsular Malaysia.

Keywords: air temperature control, interpolation analysis, peninsular Malaysia, regression model, air temperature

Procedia PDF Downloads 375