Search results for: multiple linear regression model

Authors: Ahmad Zulfahmi Mohd Kamaruzaman, Mohd Ismail Ibrahim, Ariffin Marzuki Mokhtar, Maizun Mohd Zain, Saiful Nazri Satiman, Mohd Najib Majdi Yaacob

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Background: Aftermath any patient safety incidents, the involved healthcare providers possibly sustained second victim-related distress (second victim distress and reduced their professional efficacy), with subsequent negative work-related outcomes or vice versa cultivating resilience. This study aimed to investigate the factors affecting negative work-related outcomes and resilience, with the triad of support; colleague, supervisor, and institutional support as the hypothetical mediators. Methods: This was a cross sectional study recruiting a total of 733 healthcare providers from three tertiary care in Kelantan, Malaysia. Three steps of hierarchical linear regression were developed for each outcome; negative work-related outcomes and resilience. Then, four multiple mediator models of support triad were analyzed. Results: Second victim distress, professional efficacy, and the support triad contributed significantly for each regression model. In the pathway of professional efficacy on each negative work-related outcomes and resilience, colleague support partially mediated the relationship. As for second victim distress on negative work related outcomes, colleague and supervisor support were the partial mediator, and on resilience; all support triad also produced a similar effect. Conclusion: Second victim distress, professional efficacy, and the support triad influenced the relationship with the negative work-related outcomes and resilience. Support triad as the mediators ameliorated the effect in between and explained the urgency of having good support for recovery post encountering patient safety incidents.

Keywords: second victims, patient safety incidents, hierarchical linear regression, mediation, support

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: B. Güney, Ç. Teke

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In recent years, rapid and correct decision making is crucial for both people and enterprises. However, uncertainty makes decision-making difficult. Fuzzy logic is used for coping with this situation. Thus, fuzzy linear programming models are developed in order to handle uncertainty in objective function and the constraints. In this study, a problem of a factory in food industry is investigated, required data is obtained and the problem is figured out as a fuzzy linear programming model. The model is solved using Zimmerman approach which is one of the approaches for fuzzy linear programming. As a result, the solution gives the amount of production for each product type in order to gain maximum profit.

Keywords: food industry, fuzzy linear programming, fuzzy logic, linear programming

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Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

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Authors: Seyedtaghi Mirmohammadi, J. Yazdani, S. M. Asadi, M. Rokni, A. Toosi

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There are many indoor air quality studies with an emphasis on indoor particulate matters (PM2.5) monitoring. Whereas, there is a lake of data about indoor PM2.5 concentrations in rural area schools (especially in classrooms), since preliminary children are assumed to be more defenseless to health hazards and spend a large part of their time in classrooms. The objective of this study was indoor PM2.5 concentration quality assessment. Fifteen preliminary schools by time-series sampling were selected to evaluate the indoor air quality in the rural district of Sari city, Iran. Data on indoor air climate parameters (temperature, relative humidity and wind speed) were measured by a hygrometer and thermometer. Particulate matters (PM2.5) were collected and assessed by Real Time Dust Monitor, (MicroDust Pro, Casella, UK). The mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3 in average. The multiple linear regression revealed that a correlation between PM2.5 concentration and relative humidity, distance from city center and classroom size. Classroom size yields reasonable negative relationship, the PM2.5 concentration was ranged from 65 to 540μg/m3 and statistically significant at 0.05 level and the relative humidity was ranged from 70 to 85% and dry bulb temperature ranged from 28 to 29°C were statistically significant at 0.035 and 0.05 level, respectively. A statistical predictive model was obtained from multiple regressions modeling for PM2.5 and indoor psychrometric parameters.

Keywords: particulate matters, classrooms, regression, concentration, humidity

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Moses Mwangi, Geert Verbeke, Geert Molenberghs

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Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment with respect to a reference treatment (placebo or standard). The main advantage of using cross-over design over conventional parallel design is its flexibility, where every subject become its own control, thereby reducing confounding effect. Jones & Kenward, discuss in detail more recent developments in the analysis of cross-over trials. We revisit the simple piecewise linear mixed-effects model, proposed by Mwangi et. al, (in press) for its first application in the analysis of cross-over trials. We compared performance of the proposed piecewise linear mixed-effects model with two commonly cited statistical models namely, (1) Grizzle model; and (2) Jones & Kenward model, used in estimation of the treatment effect, in the analysis of randomized cross-over trial. We estimate two performance measurements (mean square error (MSE) and coverage probability) for the three methods, using data simulated from the proposed piecewise linear mixed-effects model. Piecewise linear mixed-effects model yielded lowest MSE estimates compared to Grizzle and Jones & Kenward models for both small (Nobs=20) and large (Nobs=600) sample sizes. It’s coverage probability were highest compared to Grizzle and Jones & Kenward models for both small and large sample sizes. A piecewise linear mixed-effects model is a better estimator of treatment effect than its two competing estimators (Grizzle and Jones & Kenward models) in the analysis of cross-over trials. The data generating mechanism used in this paper captures two time periods for a simple 2-Treatments x 2-Periods cross-over design. Its application is extendible to more complex cross-over designs with multiple treatments and periods. In addition, it is important to note that, even for single response models, adding more random effects increases the complexity of the model and thus may be difficult or impossible to fit in some cases.

Keywords: Evaluation, Grizzle model, Jones & Kenward model, Performance measures, Simulation

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Authors: Mehdi Mirzaei Alavijeh, Farzad Jalilian

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Background: Marijuana is the most widely used illicit drug worldwide, especially among adolescents and young adults, which can cause numerous complications. The aim of this study was to determine the pattern, motivation use, and factors related to marijuana use among Iranian youths based on the integrative model of behavioral prediction Methods: A cross-sectional study was conducted among 174 youths marijuana user in Kermanshah County and Isfahan County, during summer 2014 which was selected with the convenience sampling for participation in this study. A self-reporting questionnaire was applied for collecting data. Data were analyzed by SPSS version 21 using bivariate correlations and linear regression statistical tests. Results: The mean marijuana use of respondents was 4.60 times at during week [95% CI: 4.06, 5.15]. Linear regression statistical showed, the structures of integrative model of behavioral prediction accounted for 36% of the variation in the outcome measure of the marijuana use at during week (R2 = 36% & P < 0.001); and among them attitude, marijuana refuse, and subjective norms were a stronger predictors. Conclusion: Comprehensive health education and prevention programs need to emphasize on cognitive factors that predict youth’s health-related behaviors. Based on our findings it seems, designing educational and behavioral intervention for reducing positive belief about marijuana, marijuana self-efficacy refuse promotion and reduce subjective norms encourage marijuana use has an effective potential to protect youths marijuana use.

Keywords: marijuana, youth, integrative model of behavioral prediction, Iran

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Authors: Raziyeh Shamsi

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In this paper, we present a multiple objective linear programming (MOLP) problem in full fuzzy case and find Data Envelopment Analysis(DEA) targets. In the presented model, we are seeking the least inputs and the most outputs in the production possibility set (PPS) with the variable return to scale (VRS) assumption, so that the efficiency projection is obtained for all decision making units (DMUs). Then, we provide an algorithm for finding DEA targets interactively in the full fuzzy case, which solves the full fuzzy problem without defuzzification. Owing to the use of interactive methods, the targets obtained by our algorithm are more applicable, more realistic, and they are according to the wish of the decision maker. Finally, an application of the algorithm in 21 educational institutions is provided.

Keywords: DEA, MOLP, full fuzzy, target

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Authors: Ghada Abo-Zaid

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Introduction: Oil is one of the most income source in Bahrain. Low oil price influence on the economy growth and the investment rate in Bahrain. For example, the economic growth was 3.7% in 2012, and it reduced to 2.9% in 2015. Investment rate was 9.8% in 2012, and it is reduced to be 5.9% and -12.1% in 2014 and 2015, respectively. The inflation rate is increased to the peak point in 2013 with 3.3 %. Objectives: The objectives here are to build statistical models to examine the effect of the interest rate inflation rate on the growth economy in Bahrain from 2000 to 2018. Methods: This study based on 18 years, and the multiple regression model is used for the analysis. All of the missing data are omitted from the analysis. Results: Regression model is used to examine the association between the Growth national product (GNP), the inflation rate, and real interest rate. We found that (i) Increase the real interest rate decrease the GNP. (ii) Increase the inflation rate does not effect on the growth economy in Bahrain since the average of the inflation rate was almost 2%, and this is considered as a low percentage. Conclusion: There is a positive impact of the real interest rate on the GNP in Bahrain. While the inflation rate does not show any negative influence on the GNP as the inflation rate was not large enough to effect negatively on the economy growth rate in Bahrain.

Keywords: growth national product, egypt, regression model, interest rate

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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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

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Authors: Prateek Singh, Dilshad Ahmad

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Growing demand for high-quality steel products has inspired researchers to investigate the unit operations involved in the manufacturing of these products (slabs, rods, sheets, etc.). One such operation is tundish operation, in which a vessel (tundish) acts as a buffer of molten steel for the solidification operation in mold. It is observed that tundish also plays a crucial role in the quality and cleanliness of the steel produced, besides merely acting as a reservoir for the mold. It facilitates removal of dissolved oxygen (inclusions) from the molten steel thus improving its cleanliness. Inclusion removal can be enhanced by increasing the residence time of molten steel in the tundish by incorporation of flow modifiers like dams, weirs, turbo-pad, etc. These flow modifiers also help in reducing the dead or short circuit zones within the tundish which is significant for maintaining thermal and chemical homogeneity of molten steel. Thus, it becomes important to analyze the flow of molten steel in the tundish for different configuration of flow modifiers. In the present work, effect of varying positions and heights/depths of dam and weir on the dead volume in tundish is studied. Steady state thermal and flow profiles of molten steel within the tundish are obtained using OpenFOAM. Subsequently, Residence Time Distribution analysis is performed to obtain the percentage of dead volume in the tundish. Design of Experiment method is then used to configure different tundish geometries for varying positions and heights/depths of dam and weir, and dead volume for each tundish design is obtained. A second-degree polynomial with two-term interactions of independent variables to predict the dead volume in the tundish with positions and heights/depths of dam and weir as variables are computed using Multiple Linear Regression model. This polynomial is then used in an optimization framework to obtain the optimal tundish geometry for minimizing dead volume using Sequential Quadratic Programming optimization.

Keywords: design of experiments, multiple linear regression, OpenFOAM, residence time distribution, sequential quadratic programming optimization, steel, tundish

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Authors: Afsaneh Mohammadnejad, Marianne Nygaard, Jan Baumbach, Shuxia Li, Weilong Li, Jesper Lund, Jacob v. B. Hjelmborg, Lene Christensen, Qihua Tan

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Cognitive impairment in the elderly is a key issue affecting the quality of life. Despite a strong genetic background in cognition, only a limited number of single nucleotide polymorphisms (SNPs) have been found. These explain a small proportion of the genetic component of cognitive function, thus leaving a large proportion unaccounted for. We hypothesize that one reason for this missing heritability is the misspecified modeling in data analysis concerning phenotype distribution as well as the relationship between SNP dosage and the phenotype of interest. In an attempt to overcome these issues, we introduced a model-free method based on the generalized correlation coefficient (GCC) in a genome-wide association study (GWAS) of cognitive function in twin samples and compared its performance with two popular linear regression models. The GCC-based GWAS identified two genome-wide significant (P-value < 5e-8) SNPs; rs2904650 near ZDHHC2 on chromosome 8 and rs111256489 near CD6 on chromosome 11. The kinship model also detected two genome-wide significant SNPs, rs112169253 on chromosome 4 and rs17417920 on chromosome 7, whereas no genome-wide significant SNPs were found by the linear mixed model (LME). Compared to the linear models, more meaningful biological pathways like GABA receptor activation, ion channel transport, neuroactive ligand-receptor interaction, and the renin-angiotensin system were found to be enriched by SNPs from GCC. The GCC model outperformed the linear regression models by identifying more genome-wide significant genetic variants and more meaningful biological pathways related to cognitive function. Moreover, GCC-based GWAS was robust in handling genetically related twin samples, which is an important feature in handling genetic confounding in association studies.

Keywords: cognition, generalized correlation coefficient, GWAS, twins

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Authors: Tunjo Perič, Marin Fatović

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In this paper a new methodology for vendor selection and supply quotas determination (VSSQD) is proposed. The problem of VSSQD is solved by the model that combines revised weighting method for determining the objective function coefficients, and a multiple objective linear programming (MOLP) method based on the cooperative game theory for VSSQD. The criteria used for VSSQD are: (1) purchase costs and (2) product quality supplied by individual vendors. The proposed methodology is tested on the example of flour purchase for a bakery with two decision makers.

Keywords: cooperative game theory, multiple objective linear programming, revised weighting method, vendor selection

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Authors: Ghada Soltane

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The objective of this study was to identify factors impacting supply chain collaboration. a quantitative study was carried out on a sample of 84 Tunisian industrial companies. To verify the research hypotheses and test the direct effect of these factors on supply chain collaboration a multiple regression method was used using SPSS 26 software. The results show that there are four factors direct effects that affect supply chain collaboration in a meaningful and positive way, including: trust, engagement, information sharing and information quality

Keywords: supply chain collaboration, factors of collaboration, principal component analysis, multiple regression

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Authors: T. Kobayashi, K. Shiba, T. Kaburagi, Y. Kurihara

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For the elderly living alone, falls can be a serious problem encountered in daily life. Some elderly people are unable to stand up without the assistance of a caregiver. They may become unconscious after a fall, which can lead to serious aftereffects such as hypothermia, dehydration, and sometimes even death. We treat the subject as an inverted pendulum and model its angle from the equilibrium position and its angular velocity. As the model is non-linear, we implement the filtering method with a particle filter which can estimate true states of the non-linear model. In order to evaluate the accuracy of the particle filter estimation results, we calculate the root mean square error (RMSE) between the estimated angle/angular velocity and the true values generated by the simulation. The experimental results give the highest accuracy RMSE of 0.0141 rad and 0.1311 rad/s for the angle and angular velocity, respectively.

Keywords: fall, microwave Doppler sensor, non-linear dynamics model, particle filter

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Authors: Nagham Ismail, Djamel Ouahrani

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Thermal resilience in the built environment reflects the building's capacity to adapt to extreme climate changes. In hot climates, power outages in office buildings pose risks to the health and productivity of workers. Therefore, it is of interest to quantify the thermal resilience of office buildings by developing a user-friendly simplified model. This simplified model begins with creating an assessment metric of thermal resilience that measures the duration between the power outage and the point at which the thermal habitability condition is compromised, considering different power interruption times (morning, noon, and afternoon). In this context, energy simulations of an office building are conducted for Qatar's summer weather by changing different parameters that are related to the (i) wall characteristics, (ii) glazing characteristics, (iii) load, (iv) orientation and (v) air leakage. The simulation results are processed using SPSS to derive linear regression equations, aiding stakeholders in evaluating the performance of commercial buildings during different power interruption times. The findings reveal the significant influence of glazing characteristics on thermal resilience, with the morning power outage scenario posing the most detrimental impact in terms of the shortest duration before compromising thermal resilience.

Keywords: thermal resilience, thermal envelope, energy modeling, building simulation, thermal comfort, power disruption, extreme weather

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Authors: Etienne Provencal, David L. St-Pierre

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

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

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Authors: Adji Achmad Rinaldo Fernandes

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Regression analysis is a statistical method that is able to describe and predict causal relationships between individuals. Not all relationships have a known curve shape; often, there are relationship patterns that cannot be known in the shape of the curve; besides that, a cause can have an impact on more than one effect, so that between effects can also have a close relationship in it. Regression analysis that can be done to find out the relationship can be brought closer to the semiparametric regression of truncated spline biresponse. The purpose of this study is to examine the function estimator and determine the best model of truncated spline biresponse semiparametric regression. The results of the secondary data study showed that the best model with the highest order of quadratic and a maximum of two knots with a Goodness of fit value in the form of Adjusted R2 of 88.5%.

Keywords: biresponse, farmer attachment, farmer loyalty, truncated spline

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Authors: Strahinja Kovacevic, Milica Karadzic Banjac, Jasmina Vitas, Stefan Vukmanovic, Radomir Malbasa, Lidija Jevric, Sanja Podunavac-Kuzmanovic

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The present study deals with chemometric regression analysis of quality parameters and the radical scavenging ability of kombucha fermented kefir-like products obtained with winter savory (WS), peppermint (P), stinging nettle (SN) and wild thyme tea (WT) kombucha inoculums. Each analyzed sample was described by milk fat content (MF, %), total unsaturated fatty acids content (TUFA, %), monounsaturated fatty acids content (MUFA, %), polyunsaturated fatty acids content (PUFA, %), the ability of free radicals scavenging (RSA Dₚₚₕ, % and RSA.ₒₕ, %) and pH values measured after each hour from the start until the end of fermentation. The aim of the conducted regression analysis was to establish chemometric models which can predict the radical scavenging ability (RSA Dₚₚₕ, % and RSA.ₒₕ, %) of the samples by correlating it with the MF, TUFA, MUFA, PUFA and the pH value at the beginning, in the middle and at the end of fermentation process which lasted between 11 and 17 hours, until pH value of 4.5 was reached. The analysis was carried out applying univariate linear (ULR) and multiple linear regression (MLR) methods on the raw data and the data standardized by the min-max normalization method. The obtained models were characterized by very limited prediction power (poor cross-validation parameters) and weak statistical characteristics. Based on the conducted analysis it can be concluded that the resulting radical scavenging ability cannot be precisely predicted only on the basis of MF, TUFA, MUFA, PUFA content, and pH values, however, other quality parameters should be considered and included in the further modeling. This study is based upon work from project: Kombucha beverages production using alternative substrates from the territory of the Autonomous Province of Vojvodina, 142-451-2400/2019-03, supported by Provincial Secretariat for Higher Education and Scientific Research of AP Vojvodina.

Keywords: chemometrics, regression analysis, kombucha, quality control

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Authors: William Chung

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This paper is to study the use of multiple subproblems in Dantzig-Wolfe decomposition of linear programming (DW-LP). Traditionally, the decomposed LP consists of one LP master problem and one LP subproblem. The master problem and the subproblem is solved alternatively by exchanging the dual prices of the master problem and the proposals of the subproblem until the LP is solved. It is well known that convergence is slow with a long tail of near-optimal solutions (asymptotic convergence). Hence, the performance of DW-LP highly depends upon the number of decomposition steps. If the decomposition steps can be greatly reduced, the performance of DW-LP can be improved significantly. To reduce the number of decomposition steps, one of the methods is to increase the number of proposals from the subproblem to the master problem. To do so, we propose to add a quadratic approximation function to the LP subproblem in order to develop a set of approximate-LP subproblems (multiple subproblems). Consequently, in each decomposition step, multiple subproblems are solved for providing multiple proposals to the master problem. The number of decomposition steps can be reduced greatly. Note that each approximate-LP subproblem is nonlinear programming, and solving the LP subproblem must faster than solving the nonlinear multiple subproblems. Hence, using multiple subproblems in DW-LP is the tradeoff between the number of approximate-LP subproblems being formed and the decomposition steps. In this paper, we derive the corresponding algorithms and provide some simple computational results. Some properties of the resulting algorithms are also given.

Keywords: approximate subproblem, Dantzig-Wolfe decomposition, large-scale models, multiple subproblems

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Authors: Firouz Amani, Adalat Hoseinian, Sajjad Hakimian

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Background: The mortality due to Myocardial Infarction (MI) is often occur during the first hours after onset of symptom. So, for taking the necessary treatment and decreasing the mortality rate, timely visited of the hospital could be effective in this regard. The aim of this study was to investigate the impact of effective factors in mortality of MI patients by using Linear Logistic Regression. Materials and Methods: In this case-control study, all patients with Acute MI who referred to the Ardabil city hospital were studied. All of died patients were considered as the case group (n=27) and we select 27 matched patients without Acute MI as a control group. Data collected for all patients in two groups by a same checklist and then analyzed by SPSS version 24 software using statistical methods. We used the linear logistic regression model to determine the effective factors on mortality of MI patients. Results: The mean age of patients in case group was significantly higher than control group (75.1±11.7 vs. 63.1±11.6, p=0.001).The history of non-cardinal diseases in case group with 44.4% significantly higher than control group with 7.4% (p=0.002).The number of performed PCIs in case group with 40.7% significantly lower than control group with 74.1% (P=0.013). The time distance between hospital admission and performed PCI in case group with 110.9 min was significantly upper than control group with 56 min (P=0.001). The mean of delay time from Onset of symptom to hospital admission (patient delay) and the mean of delay time from hospital admissions to receive treatment (system delay) was similar between two groups. By using logistic regression model we revealed that history of non-cardinal diseases (OR=283) and the number of performed PCIs (OR=24.5) had significant impact on mortality of MI patients in compare to other factors. Conclusion: Results of this study showed that of all studied factors, the number of performed PCIs, history of non-cardinal illness and the interval between onset of symptoms and performed PCI have significant relation with morality of MI patients and other factors were not meaningful. So, doing more studies with a large sample and investigated other involved factors such as smoking, weather and etc. is recommended in future.

Keywords: acute MI, mortality, heart failure, arrhythmia

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Authors: Khadidja Bellifa, Sidi Mohamed Mekelleche

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Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified.

Keywords: halogenated phenols, toxicity mechanism, hydrophobicity, electrophilicity index, quantitative stucture-toxicity relationships

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Authors: Mehdi Moeinaddini, Zohreh Asadi-Shekari, Muhammad Zaly Shah, Amran Hamzah

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Currently, planners try to have more green travel options to decrease economic, social and environmental problems. Therefore, this study tries to find significant urban travel factors to be used to increase the usage of alternative urban travel modes. This paper attempts to identify the relationship between prominent urban mobility indicators and daily trips by public transport in 30 cities from various parts of the world. Different travel modes, infrastructures and cost indicators were evaluated in this research as mobility indicators. The results of multi-linear regression analysis indicate that there is a significant relationship between mobility indicators and the daily usage of public transport.

Keywords: green travel modes, urban travel indicators, daily trips by public transport, multi-linear regression analysis

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Authors: Intan Maisarah Abd Rahim, Herlina Abdul Rahim, Rashidah Ghazali

Abstract:

Diabetes is a medical condition that can lead to various diseases such as stroke, heart disease, blindness and obesity. In clinical practice, the concern of the diabetic patients towards the blood glucose examination is rather alarming as some of the individual describing it as something painful with pinprick and pinch. As for some patient with high level of glucose level, pricking the fingers multiple times a day with the conventional glucose meter for close monitoring can be tiresome, time consuming and painful. With these concerns, several non-invasive techniques were used by researchers in measuring the glucose level in blood, including ultrasonic sensor implementation, multisensory systems, absorbance of transmittance, bio-impedance, voltage intensity, and thermography. This paper is discussing the application of the near-infrared (NIR) spectroscopy as a non-invasive method in measuring the glucose level and the implementation of the linear system identification model in predicting the output data for the NIR measurement. In this study, the wavelengths considered are at the 1450 nm and 1950 nm. Both of these wavelengths showed the most reliable information on the glucose presence in blood. Then, the linear Autoregressive Moving Average Exogenous model (ARMAX) model with both un-regularized and regularized methods was implemented in predicting the output result for the NIR measurement in order to investigate the practicality of the linear system in this study. However, the result showed only 50.11% accuracy obtained from the system which is far from the satisfying results that should be obtained.

Keywords: diabetes, glucose level, linear, near-infrared, non-invasive, prediction system

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Authors: Katja Siefken, Astrid Junge

Abstract:

Background: Research indicates that physical activity (PA) protects us from developing mental disorders. The knowledge regarding optimal domain, intensity, type, context, and amount of PA promotion for the prevention of mental disorders is sparse and incoherent. The objective of this study is to determine the relationship between PA domains and mental well-being, and whether associations vary by domain, amount, context, intensity, and type of PA. Methods: 310 individuals (age: 25 yrs., SD 7; 73% female) completed a questionnaire on personal patterns of their PA behaviour (IPQA) and their mental health (Centre of Epidemiologic Studies Depression Scale (CES-D), Generalized Anxiety Disorder (GAD-7) scale, the subjective physical well-being (FEW-16)). Linear and multiple regression were used for analysis. Findings: Individuals who met the PA recommendation (N=269) reported higher scores on subjective physical well-being than those who did not meet the PA recommendations (N=41). Whilst vigorous intensity PA predicts subjective well-being (β = .122, p = .028), it also correlates with depression. The more vigorously physically active a person is, the higher the depression score (β = .127, p = .026). The strongest impact of PA on mental well-being can be seen in the transport domain. A positive linear correlation on subjective physical well-being (β =.175, p = .002), and a negative linear correlation for anxiety (β =-.142, p = .011) and depression (β = -.164, p = .004) was found. Multiple regression analysis indicates similar results: Time spent in active transport on the bicycle significantly lowers anxiety and depression scores and enhances subjective physical well-being. The more time a participant spends using the bicycle for transport, the lower the depression (β = -.143, p = .013) and anxiety scores (β = -.111,p = .050). Conclusions: Meeting the PA recommendations enhances subjective physical well-being. Active transport has a substantial impact on mental well-being. Findings have implications for policymakers, employers, public health experts and civil society. A stronger focus on the promotion and protection of health through active transport is recommended. Inter-sectoral exchange, outside the health sector, is required. Health systems must engage other sectors in adopting policies that maximize possible health gains.

Keywords: active transport, mental well-being, health promotion, psychological disorders

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Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

Abstract:

This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures.

Keywords: traffic congestion, multistate speed distribution, traffic occupancy, Dirichlet process mixtures of generalized linear model, Bayesian change-point detection

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Authors: Adriano Z. Zambom, Preethi Ravikumar

Abstract:

One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified.

Keywords: additive model, nonparametric regression, variable selection, Akaike Information Criteria

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Authors: Jude Opara, Esemokumo Perewarebo Akpos

Abstract:

This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results.

Keywords: Theil’s regression, Bayesian information criterion, Akaike information criterion, OLS

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