Search results for: regression coefficient

Authors: Seydou Sinde

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The aims of this paper are to formulate mathematical expressions that can be used to estimate the standpipe pressure (SPP). The developed formulas take into account the main factors that, directly or indirectly, affect the behavior of SPP values. Fluid rheology and well hydraulics are some of these essential factors. Mud Plastic viscosity, yield point, flow power, consistency index, flow rate, drillstring, and annular geometries are represented by the frictional pressure (Pf), which is one of the input independent parameters and is calculated, in this paper, using Herschel-Bulkley rheological model. Other input independent parameters include the rate of penetration (ROP), applied load or weight on the bit (WOB), bit revolutions per minute (RPM), bit torque (TRQ), and hole inclination and direction coupled in the hole curvature or dogleg (DL). The technique of repeating parameters and Buckingham PI theorem are used to reduce the number of the input independent parameters into the dimensionless revolutions per minute (RPMd), the dimensionless torque (TRQd), and the dogleg, which is already in the dimensionless form of radians. Multivariable linear and polynomial regression technique using PTC Mathcad Prime 4.0 is used to analyze and determine the exact relationships between the dependent parameter, which is SPP, and the remaining three dimensionless groups. Three models proved sufficiently satisfactory to estimate the standpipe pressure: multivariable linear regression model 1 containing three regression coefficients for vertical wells; multivariable linear regression model 2 containing four regression coefficients for deviated wells; and multivariable polynomial quadratic regression model containing six regression coefficients for both vertical and deviated wells. Although that the linear regression model 2 (with four coefficients) is relatively more complex and contains an additional term over the linear regression model 1 (with three coefficients), the former did not really add significant improvements to the later except for some minor values. Thus, the effect of the hole curvature or dogleg is insignificant and can be omitted from the input independent parameters without significant losses of accuracy. The polynomial quadratic regression model is considered the most accurate model due to its relatively higher accuracy for most of the cases. Data of nine wells from the Middle East were used to run the developed models with satisfactory results provided by all of them, even if the multivariable polynomial quadratic regression model gave the best and most accurate results. Development of these models is useful not only to monitor and predict, with accuracy, the values of SPP but also to early control and check for the integrity of the well hydraulics as well as to take the corrective actions should any unexpected problems appear, such as pipe washouts, jet plugging, excessive mud losses, fluid gains, kicks, etc.

Keywords: standpipe, pressure, hydraulics, nondimensionalization, parameters, regression

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Authors: Hyon I. Paek, Sang Rim Kim, Hyon A. Ryu

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In functional linear regression, one typical problem is to reduce dimension. Compared with multivariate linear regression, functional linear regression is regarded as an infinite-dimensional case, and the main task is to reduce dimensions of functional response and functional predictors. One common approach is to adapt functional principal component analysis (FPCA) on functional predictors and then use a few leading functional principal components (FPC) to predict the functional model. The leading FPCs estimated by the typical FPCA explain a major variation of the functional predictor, but these leading FPCs may not be mostly correlated with the functional response, so they may not be significant in the prediction for response. In this paper, we propose a supervised functional principal component analysis method for a functional response model with FPCs obtained by considering the correlation of the functional response. Our method would have a better prediction accuracy than the typical FPCA method.

Keywords: supervised, functional principal component analysis, functional response, functional linear regression

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Authors: Y. Yongli, M. H. Aissa

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The purpose of this paper aims for a geotechnical analysis based on experimental physical and mechanical characteristics of Miocene marl situated at Medea region in Algeria. More than 150 soil samples were taken in the investigation part of the North-South Highway which extends over than 53 km from Chiffa in the North to Berrouaghia in the South of Algeria. The analysis of data in terms of Atterberg limits, plasticity index, and clay content reflects an acceptable correlation justified by a high coefficient of regression which was compared with the previous works in the region. Finally, approximated equations that serve as a guideline for geotechnical design locally have been suggested.

Keywords: correlation, geotechnical properties, miocene marl, north-south highway

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Authors: Irfan Ahmad Afip

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This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring.

Keywords: geographical information system, hydrometeorological, leptospirosis, multivariate regression

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Authors: Esmat Mohammadinasab

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The aim of this paper is to investigate theoretically and establish a predictive model for determination LogP of armchair polyhex BN nanotubes by using simple descriptors. The relationship between the octanol-water partition coefficient (LogP) and quantum chemical descriptors, electric moments, and topological indices of some armchair polyhex BN nanotubes with various lengths and fixed circumference are represented. Based on density functional theory (DFT) electric moments and physico-chemical properties of those nanotubes are calculated. The DFT method performed based on the Becke’s 3-parameter formulation with the Lee-Yang-Parr functional (B3LYP) method and 3-21G standard basis sets. For the first time, the relationship between partition coefficient and different properties of polyhex BN nanotubes is investigated.

Keywords: topological indices, quantum descriptors, DFT method, nanotubes

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Authors: A. Elbatran, Y. Ahmed, A. Radwan, M. Ishak

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The use of WIG craft is representing an ambitious technology that will support in reducing time, effort, and money of the conventional marine transportation in the future. This paper investigates the aerodynamic characteristic of compound wing-in-ground effect (WIG) craft model. Drag coefficient, lift coefficient and Lift and drag ratio were studied numerically with respect to the ground clearance and the wing angle of attack. The modifications of the wing has been done in order to investigate the most suitable wing configuration that can increase the wing lift-to-drag ratio at low ground clearance. A numerical investigation was carried out in this research work using finite volume Reynolds-Averaged Navier-Stokes Equations (RANSE) code ANSYS CFX, Validation was carried out by using experiments. The experimental and the numerical results concluded that the lift to drag ratio decreased with the increasing of the ground clearance.

Keywords: drag Coefficient, ground clearance, navier-stokes, WIG

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Authors: Encarnación Álvarez, Rosa M. García-Fernández, Juan F. Muñoz, Francisco J. Blanco-Encomienda

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The problem of estimating a proportion has important applications in the field of economics, and in general, in many areas such as social sciences. A common application in economics is the estimation of the headcount index. In this paper, we define the general headcount index as a proportion. Furthermore, we introduce a new quantitative method for estimating the headcount index. In particular, we suggest to use the logistic regression estimator for the problem of estimating the headcount index. Assuming a real data set, results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the traditional estimator of the headcount index.

Keywords: poverty line, poor, risk of poverty, Monte Carlo simulations, sample

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Authors: Md Rezaul Karim, Farha Taznin

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The purpose of this study was to assess and describe the individual and household characteristics of farmers, to measure the attitude of farmers towards climate adaptation farming practices and to explore the individual and household factors contributing in predicting their attitude towards climate adaptation farming practices. Data were collected through personal interviews using a pre-tested interview schedule. The data collection was done at Biral Upazila under Dinajpur district in Bangladesh from 1st November to 15 December 2018. Besides descriptive statistical parameters, Pearson’s Product Moment Correlation Coefficient (r), multiple regression and step-wise multiple regression analysis were used for the statistical analysis. Findings indicated that the highest proportion (77.6 percent) of the farmers had moderately favorable attitudes, followed by only 11.2 percent with highly favorable attitudes and 11.2 percent with slightly favorable attitudes towards climate adaptation farming practices. According to the computed correlation coefficients (r), among the 10 selected factors, five of them, such as education of household head, farm size, annual household income, organizational participation, and information access by extension services, had a significant relationship with the attitude of farmers towards climate-smart practices. The step-wise multiple regression results showed that two characteristics as education of household head and information access by extension services, contributed 26.2% and 5.1%, respectively, in predicting farmers' attitudes towards climate adaptation farming practices. In addition, more than two-thirds of farmers cited their opinion to the problems in response to ‘price of vermi species is high and it is not easily available’ as 1st ranked problem, followed by ‘lack of information for innovative climate-smart technologies’. This study suggests that policy implications are necessary to promote extension education and information services and overcome the obstacles to climate adaptation farming practices. It further recommends that research study should be conducted in diverse contexts of nationally or globally.

Keywords: factors, attitude, climate adaptation, farming practices, Bangladesh

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Authors: S. Dey, T. Mukhopadhyay, S. Adhikari

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This paper presents an exhaustive comparative investigation on sampling techniques of polynomial regression model based stochastic natural frequency of composite plates. Both individual and combined variations of input parameters are considered to map the computational time and accuracy of each modelling techniques. The finite element formulation of composites is capable to deal with both correlated and uncorrelated random input variables such as fibre parameters and material properties. The results obtained by Polynomial regression (PR) using different sampling techniques are compared. Depending on the suitability of sampling techniques such as 2k Factorial designs, Central composite design, A-Optimal design, I-Optimal, D-Optimal, Taguchi’s orthogonal array design, Box-Behnken design, Latin hypercube sampling, sobol sequence are illustrated. Statistical analysis of the first three natural frequencies is presented to compare the results and its performance.

Keywords: composite plate, natural frequency, polynomial regression model, sampling technique, uncertainty quantification

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Authors: Omar Dib, Rita Yaacoub, Luc Eveleigh, Nathalie Locquet, Hussein Dib, Ali Bassal, Christophe B. Y. Cordella

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The potential of front-face fluorescence coupled with chemometric techniques, namely parallel factor analysis (PARAFAC) and multiple linear regression (MLR) as a rapid analysis tool to characterize Lebanese virgin olive oils was investigated. Fluorescence fingerprints were acquired directly on 102 Lebanese virgin olive oil samples in the range of 280-540 nm in excitation and 280-700 nm in emission. A PARAFAC model with seven components was considered optimal with a residual of 99.64% and core consistency value of 78.65. The model revealed seven main fluorescence profiles in olive oil and was mainly associated with tocopherols, polyphenols, chlorophyllic compounds and oxidation/hydrolysis products. 23 MLR regression models based on PARAFAC scores were generated, the majority of which showed a good correlation coefficient (R > 0.7 for 12 predicted variables), thus satisfactory prediction performances. Acid values, peroxide values, and Delta K had the models with the highest predictions, with R values of 0.89, 0.84 and 0.81 respectively. Among fatty acids, linoleic and oleic acids were also highly predicted with R values of 0.8 and 0.76, respectively. Factors contributing to the model's construction were related to common fluorophores found in olive oil, mainly chlorophyll, polyphenols, and oxidation products. This study demonstrates the interest of front-face fluorescence as a promising tool for quality control of Lebanese virgin olive oils.

Keywords: front-face fluorescence, Lebanese virgin olive oils, multiple Linear regressions, PARAFAC analysis

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Yoonsuh Jung, Steven N. MacEachern

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Check loss function is used to define quantile regression. In the prospect of cross validation, it is also employed as a validation function when underlying truth is unknown. However, our empirical study indicates that the validation with check loss often leads to choosing an over estimated fits. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. It has a large effect of guarding against over fitted model in some extent. Through various simulation settings of linear and non-linear regressions, the improvement of check loss by L2 adjustment is empirically examined. This adjustment is devised to shrink to zero as sample size grows.

Keywords: cross-validation, model selection, quantile regression, tuning parameter selection

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Authors: H. Aissa, L. Mouzai, M. Bouhadef

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The effects of down slope steepness on soil splash distribution under a water drop impact have been investigated in this study. The equipment used are the burette to simulate a water drop, a splash cup filled with sandy soil which forms the source area and a splash board to collect the ejected particles. The results found in this study have shown that the apparent mass increased with increasing downslope angle following a linear regression equation with high coefficient of determination. In the same way, the radial soil splash distribution over the distance has been analyzed statistically, and an exponential function was the best fit of the relationship for the different slope angles. The curves and the regressions equations validate the well known FSDF and extend the theory of Van Dijk.

Keywords: splash distribution, water drop, slope steepness, soil detachment

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Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

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Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Austin Ikechukwu Gbasouzor, Sam Nna Omenyi, Sabuj Malli

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This study is an extension of the previous work done with ARS-680 Environmental Chamber. Drying is a complex operation that demands much energy and time. Drying is essentially important for preservation of ginger rhizome. Drying of ginger was modeled, and then the effective diffusion coefficient and activation energy where determined. For this purpose, the experiments were done at six levels of varied temperature ranging from (10, 20, 30, 40, 50, 60°C). The average effective diffusion coefficient for their studies samples for temperature range of 40°C to 70°C was 4.48 x10-10m²/s, 4.96 x10-10m²/s, and 5.31 x10-10m²/s for 0.8, 1.5 and 3m/s drying air velocity respectively. These values closely agreed with the values of effective diffusion coefficients obtained in these studies for the variously treated ginger rhizomes and test conducted.

Keywords: activation energy, diffusion coefficients, drying model, drying time, ginger rhizomes, moisture ratio, thin layer

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Authors: Hamza Shams, Sajid Saleem, Bilal A. Siddiqui

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This paper investigates the coefficient of friction at nano-levels of commercially available superhydrophobic/oleophobic coatings when applied over 316L SS. 316L Stainless Steel or Marine Stainless Steel has been selected for its widespread uses in structures, marine and biomedical applications. The coatings were investigated in harsh sand-storm and sea water environments. The particle size of the sand during the procedure was carefully selected to simulate sand-storm conditions. Sand speed during the procedure was carefully modulated to simulate actual wind speed during a sand-storm. Sample preparation was carried out using prescribed methodology by the coating manufacturer. The coating’s adhesion and thickness was verified before and after the experiment with the use of Scanning Electron Microscopy (SEM). The value for nano-level coefficient of friction has been determined using Lateral Force Microscopy (LFM). The analysis has been used to formulate a value of friction coefficient which in turn is associative of the amount of wear the coating can bear before the exposure of the base substrate to the harsh environment. The analysis aims to validate the coefficient of friction value as marketed by the coating manufacturers and more importantly test the coating in real-life applications to justify its use. It is expected that the coating would resist exposure to the harsh environment for a considerable amount of time. Further, it would prevent the sample from getting corroded in the process.

Keywords: 316L SS, scanning electron microscopy, lateral force microscopy, marine stainless steel, oleophobic coating, superhydrophobic coating

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Authors: K. K. Saijaand, K. Prabhakaran Nair

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This study aims to determine change in optimal lo-cations of dual trailing-edge flaps for various thrust coefficient to solidity (Ct /σ) ratios of helicopter to achieve minimum hub vibration levels, with low penalty in terms of required trailing-edge flap control power. Polynomial response functions are used to approximate hub vibration and flap power objective functions. Single objective and multi-objective optimization is carried with the objective of minimizing hub vibration and flap power. The optimization results shows that the inboard flap location at low Ct/σ ratio move farther from the baseline value and at high Ct/σ ratio move towards the root of the blade for minimizing hub vibration.

Keywords: helicopter rotor, trailing-edge flap, thrust coefficient to solidity (Ct /σ) ratio, optimization

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Authors: Y. H. Wu, Ji-Yuan Lin, Yu-Ming Liou

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This study aims to set up the landslide susceptibility map of County Route 89 at Ren-Ai Township in Nantou County using the Instability Index Method and Logistic regression. Seven susceptibility factors including Slope Angle, Aspect, Elevation, Distance to fold, Distance to River, Distance to Road and Accumulated Rainfall were obtained by GIS based on the Typhoon Toraji landslide area identified by Industrial Technology Research Institute in 2001. To calculate the landslide percentage of each factor and acquire the weight and grade the grid by means of Instability Index Method. In this study, landslide susceptibility can be classified into four grades: high, medium high, medium low and low, in order to determine the advantages and disadvantages of the two models. The precision of this model is verified by classification error matrix and SRC curve. These results suggest that the logistic regression model is a preferred method than instability index in the assessment of landslide susceptibility. It is suitable for the landslide prediction and precaution in this area in the future.

Keywords: instability index method, logistic regression, landslide susceptibility, SRC curve

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Authors: Wakayama Shunya, Okubo Kazuya, Fujii Toru, Sakata Daisuke, Kado Noriyuki, Furutachi Hiroshi

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The purpose of this study is to propose an effective method to improve frictional coefficient of modified shoe rubber soles with added glass fibers onto the icy and snowy road surfaces in order to prevent slip-and-fall accidents by the users. Added fibers in the rubber were uniformly tilted to the perpendicular direction of the frictional surface, where tilting angle was -60, -30, +30, +60, 90 degrees and 0 for usual specimen, respectively. It was found that horizontal arraignment was effective to improve the frictional coefficient when glass fibers were embedded in the shoe rubber, while the standing in normal direction of the embedded glass fibers on the shoe surface was also effective to do that once after they were exposed from the shoe rubber with its abrasion. These improvements were explained by the increase of stiffness against the shear deformation of the rubber at the critical frictional state and the enlargement of resistance force for extracting exposed fibers from the ice and snow, respectively. Current study suggested that effective arraignments in the tilting angle of the added fibers should be applied in designing rubber shoe soles to keep the safeties for uses in regions of cold climates.

Keywords: frictional coefficient, shoe soles, icy and snowy road, glass fibers, tilting angle

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Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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Authors: Sadia Siddiqa, Z. Khan, M. A. Hossain

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In this article, the effect of thermal radiation on mixed convection boundary layer flow of a viscous fluid along a highly heated vertical flat plate is considered with varying density and volumetric expansion coefficient. The density of the fluid is assumed to vary exponentially with temperature, however; volumetric expansion coefficient depends linearly on temperature. Boundary layer equations are transformed into convenient form by introducing primitive variable formulations. Solutions of transformed system of equations are obtained numerically through implicit finite difference method along with Gaussian elimination technique. Results are discussed in view of various parameters, like thermal radiation parameter, volumetric expansion parameter and density variation parameter on the wall shear stress and heat transfer rate. It is concluded from the present investigation that increase in volumetric expansion parameter decreases wall shear stress and enhances heat transfer rate.

Keywords: thermal radiation, mixed convection, variable density, variable volumetric expansion coefficient

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Authors: A. Nazilah

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Floods are classified among sudden onset phenomenon and the highest natural disasters happen in Malaysia. Floods have a negative impact on mental health. Measuring the psychopathology symptoms among flood victims is an important step for intervention and treatment. However, there is a gap of a valid, reliable and an efficient instrument to measure flood victims' mental health, especially in Malaysia. This study aims to replicate the earlier studies of developing e-Psychological Instrument for Flood Victims (e-PIFV). The e-PIFV is a digital self-report inventory that has 84 items with 4 dimension scales namely stress, anxiety, depression, and trauma. Two replicated studies have been done to validate the instrument using expert judgment method. Results showed that content coefficient validity for each sub-scale of the instrument ranging from moderate to very strong validity. In study I, coefficient values of stress was 0.7, anxiety was 0.9, depression was 1.0, trauma was 0.6 and overall was 0.8. In study II, the coefficient values for two subscales and overall scale were increased. The coefficient value of stress was 0.8, anxiety was 0.9, depression was 1.0, trauma was 0.8 and overall was 0.9. This study supports the theoretical framework and provides practical implication in the field of clinical psychology and flood management.

Keywords: developing e-psychological instrument, content validity, instrument, mental health management, flood victims, psychopathology, validity

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Authors: Yessi Nirwana Kurniadi, Nira Yunita Permata

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Coastline is vulnerable to coastal erosion which damage infrastructure and buildings. Floating breakwaters are applied in order to minimize material cost but still can reduce wave height. In this paper, we investigated floating breakwater anchored by piles based on experimental study in the laboratory with model scale 1:8. Two type of floating model were tested with several combination wave height, wave period and surface water elevation to determined transmission coefficient. This experimental study proved that floating breakwater with piles can prevent wave height up to 27 cm. The physical model shows that ratio of depth to wave length is less than 0.6 and ratio of model width to wave length is less than 0.3. It is confirmed that if those ratio are less than those value, the transmission coefficient is 0.5. The result also showed that the first type model of floating breakwater can reduce wave height by 60.4 % while the second one can reduce up to 55.56 %.

Keywords: floating breakwater, experimental study, pile, transimission coefficient

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: G. Ravi Kiran, G. Radhakrishnamacharya

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In this paper, the dispersion of a solute in the peristaltic flow of a Jeffrey fluid in the presence of both homogeneous and heterogeneous chemical reactions has been discussed. The average effective dispersion coefficient has been found using Taylor's limiting condition under long wavelength approximation. It is observed that the average dispersion coefficient increases with amplitude ratio which implies that dispersion is more in the presence of peristalsis. The average effective dispersion coefficient increases with Jeffrey parameter in the cases of both homogeneous and combined homogeneous and heterogeneous chemical reactions. Further, dispersion decreases with a phase difference, homogeneous reaction rate parameters, and heterogeneous reaction rate parameter.

Keywords: peristalsis, dispersion, chemical reaction, Jeffrey fluid, asymmetric channel

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Authors: Mingjiang Dai, Qian Shi, Fang Hu, Songsheng Lin, Huijun Hou, Chunbei Wei

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A diamond-like carbon (DLC) is considered as a promising protective film since its high hardness and excellent tribological properties. However, DLC films are very sensitive to the environmental condition, its friction coefficient could dramatic change in high humidity, therefore, limited their further application in aerospace, the watch industry, and micro/nano-electromechanical systems. Therefore, most studies focus on the low friction coefficient of DLC films at a high humid environment. However, this is out of satisfied in practical application. An important thing was ignored is that the DLC coated components are usually used in the diversed environment, which means its friction coefficient may evidently change in different humid condition. As a result, the invalidation of DLC coated components or even sometimes disaster occurred. For example, DLC coated minisize gears were used in the watch industry, and the customer may frequently transform their locations with different weather and humidity even in one day. If friction coefficient is not stable in dry and high moisture conditions, the watch will be inaccurate. Thus, it is necessary to investigate the stable tribological behavior of DLC films in various environments. In this study, a-C:H:Si films were deposited by multi-function magnetron sputtering system, containing one ion source device and a pair of SiC dual mid-frequent targets and two direct current Ti/C targets. Hydrogenated carbon layers were manufactured by sputtering the graphite target in argon and methane gasses. The silicon was doped in DLC coatings by sputtering silicon carbide targets and the doping content were adjusted by mid-frequent sputtering current. The microstructure of the film was characterized by Raman spectrometry, X-ray photoelectron spectroscopy, and transmission electron microscopy while its friction behavior under different humidity conditions was studied using a ball-on-disc tribometer. The a-C:H films with Si content from 0 to 17at.% were obtained and the influence of Si content on the structure and tribological properties under the relative humidity of 50% and 85% were investigated. Results show that the a-C:H:Si film has typical diamond-like characteristics, in which Si mainly existed in the form of Si, SiC, and SiO2. As expected, the friction coefficient of a-C:H films can be effectively changed after Si doping, from 0.302 to 0.176 in RH 50%. The further test shows that the friction coefficient value of a-C:H:Si film in RH 85% is first increase and then decrease as a function of Si content. We found that the a-C:H:Si films with a Si content of 3.75 at.% show a stable friction coefficient of 0.13 in different humidity environment. It is suggestion that the sp3/sp2 ratio of a-C:H films with 3.75 at.% Si was higher than others, which tend to form the silica-gel-like sacrificial layers during friction tests. Therefore, the films deliver stable low friction coefficient under controlled RH value of 50 and 85%.

Keywords: diamond-like carbon, Si doping, moisture environment, table low friction coefficient

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

Abstract:

Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Deyadeen Ali Alshibani

Abstract:

In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

Procedia PDF Downloads 421

Authors: Mohammad Farooq Wani

Abstract:

Tribological studies on nano-sized ß-silicon nitride+5% BN were carried out in dry air at high temperatures to clarify the lack of consensus in the bibliographic data concerning the Tribological behavior of Si3N4 ceramics and effect of doped hexagonal boron nitride on coefficient of friction and wear coefficient at different loads and elevated temperatures. The composites were prepared via high energy mechanical milling and subsequent spark plasma sintering using Y2O3 and Al2O3 as sintering additives. After sintering, the average crystalline size of Si3N4 was observed to be 50 nm. Tribological tests were performed with temperature and Friction coefficients 0.16 to 1.183 and 0.54 to 0.71 were observed for Nano-sized ß-silicon nitride+5% BN composite under normal load of 10N-70 N and over high temperature range of 350 ºC-550 ºC respectively. Specific wear coefficients from 1.33x 10-4 mm3N-1m-1 to 4.42x 10-4 mm3N-1m-1 were observed for Nano-sized Si3N4 + 5% BN composite against Si3N4 ball as tribo-pair counterpart over high temperature range of 350 ºC-550 ºC while as under normal load of 10N to70N Specific wear coefficients of 6.91x 10-4 mm3N-1m-1 to 1.70x 10-4 were observed. The addition of BN to the Si3N4 composite resulted in a slight reduction of the friction coefficient and lower values of wear coefficient.

Keywords: ceramics, tribology, friction and wear, solid lubrication

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Authors: Jihye Jeon

Abstract:

This paper analyzes the conceptual framework of three statistical methods, multiple regression, path analysis, and structural equation models. When establishing research model of the statistical modeling of complex social phenomenon, it is important to know the strengths and limitations of three statistical models. This study explored the character, strength, and limitation of each modeling and suggested some strategies for accurate explaining or predicting the causal relationships among variables. Especially, on the studying of depression or mental health, the common mistakes of research modeling were discussed.

Keywords: multiple regression, path analysis, structural equation models, statistical modeling, social and psychological phenomenon

Procedia PDF Downloads 598